Update parquet files (step 37 of 121)

spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/__init__.py

@@ -1,158 +0,0 @@
-import string
-import time
-import re
-import json
-import requests
-import fake_useragent
-import random
-from password_generator import PasswordGenerator
-
-from .utils import create_email, check_email
-
-
-class Account:
-    @staticmethod
-    def create(logging: bool = False):
-        is_custom_domain = input(
-            "Do you want to use your custom domain name for temporary email? [Y/n]: "
-        ).upper()
-
-        if is_custom_domain == "Y":
-            mail_address = create_email(custom_domain=True, logging=logging)
-        elif is_custom_domain == "N":
-            mail_address = create_email(custom_domain=False, logging=logging)
-        else:
-            print("Please, enter either Y or N")
-            return
-
-        name = string.ascii_lowercase + string.digits
-        username = "".join(random.choice(name) for i in range(20))
-
-        pwo = PasswordGenerator()
-        pwo.minlen = 8
-        password = pwo.generate()
-
-        session = requests.Session()
-
-        register_url = "https://ai.usesless.com/api/cms/auth/local/register"
-        register_json = {
-            "username": username,
-            "password": password,
-            "email": mail_address,
-        }
-        headers = {
-            "authority": "ai.usesless.com",
-            "accept": "application/json, text/plain, */*",
-            "accept-language": "en-US,en;q=0.5",
-            "cache-control": "no-cache",
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "user-agent": fake_useragent.UserAgent().random,
-        }
-        register = session.post(register_url, json=register_json, headers=headers)
-        if logging:
-            if register.status_code == 200:
-                print("Registered successfully")
-            else:
-                print(register.status_code)
-                print(register.json())
-                print("There was a problem with account registration, try again")
-
-        if register.status_code != 200:
-            quit()
-
-        while True:
-            time.sleep(5)
-            messages = check_email(mail=mail_address, logging=logging)
-
-            # Check if method `message_list()` didn't return None or empty list.
-            if not messages or len(messages) == 0:
-                # If it returned None or empty list sleep for 5 seconds to wait for new message.
-                continue
-
-            message_text = messages[0]["content"]
-            verification_url = re.findall(
-                r"http:\/\/ai\.usesless\.com\/api\/cms\/auth\/email-confirmation\?confirmation=\w.+\w\w",
-                message_text,
-            )[0]
-            if verification_url:
-                break
-
-        session.get(verification_url)
-        login_json = {"identifier": mail_address, "password": password}
-        login_request = session.post(
-            url="https://ai.usesless.com/api/cms/auth/local", json=login_json
-        )
-
-        token = login_request.json()["jwt"]
-        if logging and token:
-            print(f"Token: {token}")
-
-        with open("account.json", "w") as file:
-            json.dump({"email": mail_address, "token": token}, file)
-            if logging:
-                print(
-                    "\nNew account credentials has been successfully saved in 'account.json' file"
-                )
-
-        return token
-
-
-class Completion:
-    @staticmethod
-    def create(
-        token: str,
-        systemMessage: str = "You are a helpful assistant",
-        prompt: str = "",
-        parentMessageId: str = "",
-        presence_penalty: float = 1,
-        temperature: float = 1,
-        model: str = "gpt-3.5-turbo",
-    ):
-        headers = {
-            "authority": "ai.usesless.com",
-            "accept": "application/json, text/plain, */*",
-            "accept-language": "en-US,en;q=0.5",
-            "cache-control": "no-cache",
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "user-agent": fake_useragent.UserAgent().random,
-            "Authorization": f"Bearer {token}",
-        }
-
-        json_data = {
-            "openaiKey": "",
-            "prompt": prompt,
-            "options": {
-                "parentMessageId": parentMessageId,
-                "systemMessage": systemMessage,
-                "completionParams": {
-                    "presence_penalty": presence_penalty,
-                    "temperature": temperature,
-                    "model": model,
-                },
-            },
-        }
-
-        url = "https://ai.usesless.com/api/chat-process"
-        request = requests.post(url, headers=headers, json=json_data)
-        request.encoding = request.apparent_encoding
-        content = request.content
-
-        response = Completion.__response_to_json(content)
-        return response
-
-
-    @classmethod
-    def __response_to_json(cls, text) -> str:
-        text = str(text.decode("utf-8"))
-
-        split_text = text.rsplit("\n", 1)
-        if len(split_text) > 1:
-            to_json = json.loads(split_text[1])
-            return to_json
-        else:
-            return None
-

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Autocad 2009 Xforce Keygen 32 Bit Free.md

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-<h1>How to Activate AutoCAD 2009 with Xforce Keygen 32 Bit</h1>
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-<h2>Conclusion</h2>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Biztree Business in a Box Product Key Crack The Ultimate Solution for Your Business Needs.md

@@ -1,100 +0,0 @@
-<br />
-<h1>Warcraft III: Reign of Chaos No CD Crack Download</h1>
- <h2>Introduction</h2>
- <p>If you are a fan of real-time strategy games, you probably have played or heard of Warcraft III: Reign of Chaos, one of the most popular and influential titles in the genre. Released in 2002 by Blizzard Entertainment, Warcraft III: Reign of Chaos is the third installment in the Warcraft series, which also includes the massively multiplayer online role-playing game World of Warcraft. In this game, you can choose from four different races: Humans, Orcs, Night Elves, and Undead, and lead them in epic battles across the fantasy world of Azeroth.</p>
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- <p>However, if you want to play Warcraft III: Reign of Chaos on your PC, you will need to have the original CD inserted in your drive every time you launch the game. This can be inconvenient and annoying for many reasons, such as having to swap CDs, wasting disk space, risking damage to your CD, or losing your CD altogether. That's why many players look for a way to play Warcraft III: Reign of Chaos without the CD, by using a no CD crack.</p>
- <p>A no CD crack is a modified version of the game's executable file that bypasses the CD check and allows you to run the game without inserting the CD. In this article, we will show you how to download and install a no CD crack for Warcraft III: Reign of Chaos, as well as the benefits and risks of using it.</p>
- <h2>How to download and install a no CD crack?</h2>
- <p>Before you download and install a no CD crack for Warcraft III: Reign of Chaos, you will need to have the game installed on your PC. You can either use the original CD or download the game from Blizzard's official website. You will also need to update the game to the latest version (1.27b) to ensure compatibility with the no CD crack.</p>
- <p>Once you have the game installed and updated, you can follow these steps to download and install a no CD crack:</p>
- <ol>
-<li>Go to a reputable website that offers no CD cracks for various games, such as GameCopyWorld or MegaGames.</li>
-<li>Search for Warcraft III: Reign of Chaos and find the no CD crack that matches your game version and language.</li>
-<li>Download the no CD crack file and extract it using a program like WinRAR or 7-Zip.</li>
-<li>Copy the extracted file (usually named war3.exe) and paste it into your game folder, where you installed Warcraft III: Reign of Chaos. You may need to overwrite the existing file.</li>
-<li>Run the game from the no CD crack file (war3.exe) and enjoy playing without the CD.</li>
-</ol>
- <h2>Benefits of using a no CD crack</h2>
- <p>Using a no CD crack for Warcraft III: Reign of Chaos can have several benefits for you as a player. Here are some of them:</p>
- <h3>Play without the CD</h3>
- the CD every time you launch the game. This can save you time and hassle, especially if you have multiple games that require CDs. You can also play the game on any PC that has the game installed, without carrying the CD with you.</p>
- <h3>Save disk space</h3>
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- <h3>Avoid scratches and damages</h3>
- <p>A no CD crack can also help you avoid scratches and damages to your CD. CDs are fragile and can be easily scratched or broken by accidents or mishandling. If your CD gets damaged, you may not be able to play the game or install it on another PC. By using a no CD crack, you can protect your CD from wear and tear and keep it in good condition.</p>
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- <h3>How to download and install a no CD crack for Warcraft III: Reign of Chaos?</h3>
- <p>To download and install a no CD crack for Warcraft III: Reign of Chaos, you will need to have the game installed and updated on your PC. Then, you can follow these steps:</p>
- <ol>
-<li>Go to a reputable website that offers no CD cracks for various games, such as GameCopyWorld or MegaGames.</li>
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-<li>Download the no CD crack file and extract it using a program like WinRAR or 7-Zip.</li>
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-<li>Run the game from the no CD crack file (war3.exe) and enjoy playing without the CD.</li>
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- <h3>Is using a no CD crack legal?</h3>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Descargar gratis imagenes movibles para easyworship consejos y recursos para mejorar tus proyecciones.md

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-
-<h1>Descargar gratis imagenes movibles para easyworship</h1>
- <p>Si eres una persona que se encarga de preparar y proyectar las presentaciones multimedia en tu iglesia, seguramente te interesa saber cómo puedes descargar gratis imagenes movibles para easyworship. En este artículo te vamos a explicar qué es easyworship, por qué usarlo, dónde encontrar y cómo descargar imagenes movibles para easyworship, cómo usarlas en las presentaciones y qué beneficios tiene usarlas. Así que sigue leyendo y descubre cómo puedes mejorar tus presentaciones con estas imágenes tan especiales.</p>
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- <h2>¿Qué es easyworship y por qué usarlo?</h2>
- <h3>Easyworship es un programa de presentación multimedia para iglesias</h3>
- <p>Easyworship es un software diseñado especialmente para crear y proyectar presentaciones multimedia en las iglesias. Con este programa puedes combinar imágenes, videos, textos, canciones y otros elementos para crear presentaciones dinámicas y profesionales que acompañen tus cultos, sermones, eventos o actividades. Easyworship es fácil de usar, compatible con varios formatos de archivos y adaptable a diferentes pantallas y proyectores.</p>
- <h3>Easyworship permite crear presentaciones dinámicas y atractivas con imágenes, videos, textos y canciones</h3>
- <p>Con easyworship puedes crear presentaciones que se adapten al tema, al estilo y al público de cada ocasión. Puedes elegir entre una gran variedad de plantillas prediseñadas o crear tus propias plantillas personalizadas. Puedes agregar imágenes, videos, textos y canciones desde tu computadora o desde internet. Puedes editar el contenido, el orden, el tiempo y la transición de cada elemento. Puedes añadir efectos especiales como sombras, reflejos, bordes o animaciones. Puedes sincronizar las letras de las canciones con la música o con los videos. Puedes mostrar versículos bíblicos o citas inspiradoras con diferentes fuentes y colores.</p>
- <h3>Easyworship ofrece una gran variedad de opciones para personalizar y adaptar las presentaciones a cada ocasión</h3>
- <p>Con easyworship puedes personalizar y adaptar tus presentaciones según tus necesidades y preferencias. Puedes configurar el tamaño, la resolución, el brillo y el contraste de la pantalla o del proyector. Puedes ajustar el volumen, el balance y el ecualizador del sonido. Puedes crear listas de reproducción con diferentes presentaciones o elementos. Puedes programar las presentaciones para que se inicien automáticamente o manualmente. Puedes controlar las presentaciones desde tu computadora o desde un dispositivo móvil.</p>
- <h2>¿Dónde encontrar y cómo descargar imagenes movibles para easyworship?</h2>
- <h3>Hay muchas fuentes en internet que ofrecen imagenes movibles para easyworship de forma gratuita</h3>
- <p>Si quieres descargar gratis imagenes movibles para easyworship, puedes encontrar muchas opciones en internet. Hay sitios web que ofrecen cientos o miles de imágenes animadas con diferentes temáticas, estilos y calidades. Algunas imágenes son libres de derechos de autor y otras requieren dar crédito al autor o a la fuente. Algunas imágenes son gratuitas y otras requieren una suscripción o un pago.</p>
- <h3>Algunas de las fuentes más populares son Pinterest, Recursos Bíblicos y EasyWorship Media</h3>
- <p>A continuación te mencionamos algunas de las fuentes más populares donde puedes descargar gratis imagenes movibles para easyworship:</p>
- <ul>
-<li>Pinterest: Es una red social donde puedes encontrar miles de imágenes animadas sobre diversos temas como naturaleza, arte, religión, humor, etc. Solo tienes que buscar "fondos animados para easyworship" o "imagenes movibles para easyworship" en el buscador y verás los resultados. Para descargar una imagen solo tienes que hacer clic derecho sobre ella y elegir la opción "guardar imagen como".</li>
-<li>Recursos Bíblicos: Es un sitio web que ofrece más de 200 imágenes animadas para easyworship con temática cristiana. Estas imágenes están hechas a una resolución apropiada para el programa y pueden ser utilizadas de diversas formas en las presentaciones de la iglesia. Para descargar un paquete de imágenes solo tienes que hacer clic en el enlace "descargar" que aparece debajo de cada imagen.</li>
-<li>EasyWorship Media: Es la tienda oficial de medios de easyworship donde puedes encontrar una gran selección de fondos animados para tus presentaciones. Estas imágenes son creadas por talentosos artistas cristianos que ofrecen su trabajo de forma gratuita o a un precio accesible. Para descargar una imagen solo tienes que hacer clic en el botón "download" que aparece debajo de cada imagen.</li>
-</ul>
- <h3>Para descargar las imagenes movibles para easyworship se debe seguir los pasos que indica cada sitio web</h3>
- <p>Para descargar gratis imagenes movibles para easyworship se debe seguir los pasos que indica cada sitio web donde se encuentran las imágenes. En general estos pasos son:</p>
- <ol>
-<li>Ingresar al sitio web donde se encuentran las imágenes.</li>
-<li>Buscar las imágenes que se desean descargar usando el buscador o navegando por las categorías.</li>
-<li>Seleccionar la imagen que se desea descargar haciendo clic sobre ella.</li>
-<li>Verificar si la imagen tiene algún requisito o condición como dar crédito al autor o pagar una cuota.</li>
-<li>Hacer clic derecho sobre la imagen y elegir la opción "guardar imagen como" o hacer clic en el botón "descargar" si lo tiene.</li>
-<li>Elegir la carpeta donde se desea guardar la imagen en la computadora.</li>
-<li>Repetir el proceso con todas las imágenes que se deseen descargar.</li>
-</ol>
- <h2>¿Cómo usar las imagenes movibles para easyworship en las presentaciones?</h2>
- <h3>Las imagenes movibles para easyworship se pueden usar como fondos, transiciones, ilustraciones o adornos</h3>
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- <h3>Se puede ajustar el tamaño, la posición, la velocidad y el efecto de las imagenes movibles para easyworship según el gusto y el propósito</h3>
- <p>Se puede ajustar el tamaño, la posición, la velocidad y el efecto de las imagenes movibles para easyworship según el gusto y el propósito que se tenga. Para ajustar el tamaño y la posición de una imagen se debe hacer clic sobre ella y arrastrar los bordes o las esquinas hasta obtener el tamaño y la posición deseada. Para ajustar la velocidad y el efecto de una imagen se debe hacer clic derecho sobre ella y elegir la opción "propiedades". Se abrirá una ventana donde se podrá modificar la duración, el ciclo, el retraso y el efecto de la imagen.</p>
- <h2>¿Qué beneficios tiene usar imagenes movibles para easyworship en las presentaciones?</h2>
- <h3>Las imagenes movibles para easyworship le dan vida y movimiento a las presentaciones</h3>
- <p>Las imagenes movibles para easyworship le dan vida y movimiento a las presentaciones al crear un ambiente dinámico y variado. Las imágenes animadas pueden expresar diferentes sensaciones, emociones, mensajes o conceptos de forma visual y creativa. Las imágenes animadas pueden complementar o contrastar con el contenido verbal o musical de la presentación. Las imágenes animadas pueden generar interés, curiosidad, sorpresa o admiración en los espectadores.</p>
- <h3>Las imagenes movibles para easyworship captan la atención y el interés de los espectadores</h3>
- <p>Las imagenes movibles para easyworship captan la atención y el interés de los espectadores al estimular sus sentidos y su memoria. Las imágenes animadas son más llamativas y memorables que las imágenes estáticas o los textos. Las imágenes animadas pueden ayudar a los espectadores a concentrarse, a comprender, a recordar o a aplicar lo que ven o escuchan en la presentación. Las imágenes animadas pueden motivar a los espectadores a participar, a interactuar o a responder a la presentación.</p>
- <h3>Las imagenes movibles para easyworship transmiten mensajes y emociones de forma visual y creativa</h3>
- <p>Las imagenes movibles para easyworship transmiten mensajes y emociones de forma visual y creativa al usar diferentes elementos como colores, formas, texturas, sonidos o movimientos. Las imágenes animadas pueden representar diferentes temas como naturaleza, arte, religión, humor, etc. Las imágenes animadas pueden reflejar diferentes estados de ánimo como alegría, paz, amor, fe, etc. Las imágenes animadas pueden inspirar diferentes acciones como alabar, orar, servir, etc.</p>
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- <p>En conclusión, descargar gratis imagenes movibles para easyworship es una forma sencilla y efectiva de mejorar tus presentaciones multimedia en tu iglesia. Con estas imágenes puedes crear presentaciones dinámicas, atractivas, personalizadas y adaptadas a cada ocasión. Con estas imágenes puedes usar easyworship como una herramienta poderosa para comunicar el mensaje de Dios de forma visual y creativa.</p>
- <h2>Preguntas frecuentes</h2>
- <ul>
-<li><b>¿Qué es easyworship?</b><br>
-Easyworship es un programa de presentación multimedia para iglesias que permite combinar imágenes, videos, textos, canciones y otros elementos para crear presentaciones dinámicas y profesionales.</li>
-<li><b>¿Dónde puedo descargar gratis imagenes movibles para easyworship?</b><br>
-Puedes descargar gratis imagenes movibles para easyworship en sitios web como Pinterest, Recursos Bíblicos o EasyWorship Media.</li>
-<li><b>¿Cómo puedo usar las imagenes movibles para easyworship en las presentaciones?</b><br>
-<ul>
-<li><b>¿Cómo puedo usar las imagenes movibles para easyworship en las presentaciones?</b><br>
-Puedes usar las imagenes movibles para easyworship como fondos, transiciones, ilustraciones o adornos. Para usarlas debes importarlas al programa y seleccionarlas en el panel de medios. Luego puedes ajustar su tamaño, posición, velocidad y efecto según tu gusto y propósito.</li>
-<li><b>¿Qué beneficios tiene usar imagenes movibles para easyworship en las presentaciones?</b><br>
-Usar imagenes movibles para easyworship tiene muchos beneficios como dar vida y movimiento a las presentaciones, captar la atención y el interés de los espectadores y transmitir mensajes y emociones de forma visual y creativa.</li>
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-<p>Call of Duty Mobile is one of the most popular and successful first-person shooter games on mobile devices. It offers a thrilling and immersive experience that fans of the franchise love. However, some players are looking for ways to get an edge over their opponents and access more content without spending real money. That's where Call of Duty Mobile Unlimited CP Mod APK comes in.</p>
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- <h2>What is Call of Duty Mobile?</h2>
-<p>Call of Duty Mobile is a mobile version of the famous FPS franchise that was released in 2019 by Activision and Tencent. It features various game modes, maps, characters, weapons, and items from different Call of Duty titles such as Modern Warfare, Black Ops, and Warzone. It also has its own original content and storylines that expand the Call of Duty universe.</p>
- <h3>Features of Call of Duty Mobile</h3>
-<p>Some of the features that make Call of Duty Mobile stand out are:</p>
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-<li>Console-quality HD graphics and sound that create a realistic and immersive atmosphere.</li>
-<li>Customizable controls, voice and text chat, and social features that allow you to communicate and play with your friends.</li>
-<li>A progression system that lets you level up your player and weapons, unlock new items, abilities, perks, skins, camos, and more.</li>
-<li>A monetization system that uses in-game currency and microtransactions to purchase cosmetics, loot boxes, battle passes, and other benefits.</li>
-<li>A regular update schedule that adds new seasonal content, events, challenges, rewards, game modes, maps, weapons, operators, and more.</li>
-</ul>
- <h3>Game modes of Call of Duty Mobile</h3>
-<p>Call of Duty Mobile has three main game modes that you can play:</p>
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-<li>Battle Royale: This mode lets you survive a 100-player experience on a large map with vehicles, weapons, items, classes, and more. You can play solo, duo, or squad and try to be the last one standing.</li>
-<li>Zombies: This mode lets you team up with other players and fight against hordes of zombies in various maps and scenarios. You can use different weapons, items, perks, and skills to survive and complete objectives.</li>
-</ul>
- <h2>What is CP and why do you need it?</h2>
-<p>CP stands for Call of Duty Points, which is the premium currency of Call of Duty Mobile. You can use CP to buy various items and benefits that can enhance your gameplay and appearance.</p>
- <h3>CP is the premium currency of Call of Duty Mobile</h3>
-<p>CP is different from Credits, which is the free currency that you can earn by playing the game, completing tasks, and opening crates. CP can only be obtained by spending real money or using special offers and promotions. You can buy CP in different amounts and packages, depending on your region and platform.</p>
- <h3>CP can be used to buy various items and benefits</h3>
-<p>Some of the things that you can buy with CP are:</p>
-<ul>
-<li>Battle Pass: This is a seasonal pass that gives you access to exclusive rewards such as weapons, operators, skins, camos, emotes, calling cards, etc. You can buy the regular Battle Pass for 220 CP or the Battle Pass Bundle for 520 CP, which also unlocks 12 tiers instantly.</li>
-<li>Crate Bundles: These are bundles of crates that contain random items such as weapons, skins, camos, emotes, etc. You can buy different types of crates such as Premium Crates, Weapon Crates, Character Crates, etc. for various amounts of CP.</li>
-<li>Lucky Draws: These are special draws that give you a chance to win rare and legendary items such as weapons, operators, skins, camos, etc. You can spin the Lucky Draw for 30 CP for the first time and then the price increases with each spin until you get all the items.</li>
-<li>Store Items: These are individual items that you can buy directly from the store such as weapons, operators, skins, camos, etc. The price varies depending on the rarity and quality of the item.</li>
-<li>CODM Championship: This is a competitive mode that lets you participate in tournaments and win prizes such as CP, Credits, weapons, skins, etc. You need to pay 200 CP to enter the CODM Championship and then you can earn back your CP and more by winning matches.</li>
-</ul>
- <h2>What is Call of Duty Mobile Unlimited CP Mod APK?</h2>
-<p>Call of Duty Mobile Unlimited CP Mod APK is a modified version of the game that gives you unlimited CP and other features that are not available in the official version. It is also known as Call of Duty Mobile Hack or Call of Duty Mobile Cheat.</p>
- <h3>It is a modified version of the game that gives you unlimited CP</h3>
-<p>The main feature of Call of Duty Mobile Unlimited CP Mod APK is that it gives you unlimited CP without spending any real money. You can use this CP to buy anything you want in the game such as Battle Passes, Crate Bundles, Lucky Draws, Store Items, CODM Championship entries, etc. You can also use this CP to upgrade your weapons and items to the max level.</p>
- <h3>It also has other features such as ESP, Aimbot, Wall Hack, etc.</h3>
-<p>Besides unlimited CP, Call of Duty Mobile Unlimited CP Mod APK also has other features that give you an unfair advantage over other players. Some of these features are:</p>
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-<li>Aimbot: This feature lets you automatically aim and shoot at the enemy's head or body with perfect accuracy and speed. This helps you to kill them instantly and win every gunfight.</li>
-<li>Wall Hack: This feature lets you shoot through walls and obstacles without any damage reduction or bullet drop. This helps you to hit the enemy even if they are hiding or cover.</li>
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- <h2>How to download and install Call of Duty Mobile Unlimited CP Mod APK?</h2>
-<p>If you want to try Call of Duty Mobile Unlimited CP Mod APK, you need to follow these steps to download and install it on your device:</p>
- <h3>Follow these steps to download and install the mod apk</h3>
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-<li>Find a reliable and safe source that provides the latest version of Call of Duty Mobile Unlimited CP Mod APK. You can search online or use the link below.</li>
-<li>Download the mod apk file and the obb data file from the source. Make sure you have enough storage space on your device.</li>
-<li>Enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources and turning it on.</li>
-<li>Locate the downloaded files on your device and tap on them to install them. You may need to grant some permissions for the installation process.</li>
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-<p>While Call of Duty Mobile Unlimited CP Mod APK may sound tempting, it is not recommended to use it for several reasons:</p>
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-<li>It violates the game's terms of service and can result in bans or legal actions. Activision and Tencent have a strict anti-cheat system that detects and punishes players who use hacks or mods. You may lose your account, progress, items, etc. or face legal consequences if you use the mod apk.</li>
-<li>It ruins the game's balance and fairness and can affect other players' enjoyment. Using hacks or mods gives you an unfair advantage over other players who play legitimately. This can make the game boring, frustrating, or unfair for them.</li>
-<li>It exposes your device and data to malware and viruses that can harm your device or steal your information. The mod apk may contain malicious code or hidden programs that can infect your device or access your data without your consent. You may risk losing your files, contacts, photos, etc. or compromising your privacy or security if you use the mod apk.</li>
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- <h2>Conclusion</h2>
-<p>Call of Duty Mobile Unlimited CP Mod APK is a modified version of the game that gives you unlimited CP and other features that are not available in the official version. It may seem like a good option for some players who want to access more content without spending real money or who want to dominate their opponents with ease. However, it is not recommended to use it as it violates the game's terms of service and can result in bans or legal actions. It also ruins the game's balance and fairness and can affect other players' enjoyment. It also exposes your device and data to malware and viruses that can harm your device or steal your information.</p>
-<p>If you want to enjoy Call of Duty Mobile in a safe and legitimate way, you should avoid using hacks or mods and play the game as intended. You can still get CP by spending real money or using special offers and promotions. You can also improve your skills and performance by practicing, learning, and following tips and tricks from other players. You can also have fun with your friends by playing together in different game modes.</p>
- <h2>FAQs</h2>
-<h3>Q1. Is Call of Duty Mobile free to play?</h3>
-<p>A1. Yes, Call of Duty Mobile is free to play on both Android and iOS devices. You can download it from Google Play Store or Apple App Store respectively. However, it also has optional in-game purchases that you can make with real money or in-game currency.</p>
- <h3>Q2. How can I get CP legitimately in Call of Duty Mobile?</h3>
-<p>A2. There are several ways to get CP legitimately in Call of Duty Mobile, such as:</p>
-<ul>
-<li>Buying CP with real money from the in-game store or the official website.</li>
-<li>Using special offers and promotions that give you CP as a bonus or a reward.</li>
-<li>Participating in the CODM Championship and winning CP as a prize.</li>
-<li>Using third-party apps or websites that offer CP as a gift or a reward for completing tasks or surveys. However, you should be careful and check the credibility and security of these sources before using them.</li>
-</ul>
- <h3>Q3. What are the best weapons and loadouts in Call of Duty Mobile?</h3>
-<p>A3. The best weapons and loadouts in Call of Duty Mobile depend on your personal preference, playstyle, game mode, map, and situation. However, some of the most popular and effective weapons and loadouts are:</p>
-<ul>
-<li>Assault Rifles: These are versatile weapons that can perform well in most ranges and situations. Some of the best assault rifles are ASM10, AK-47, DR-H, BK57, and M4.</li>
-<li>Submachine Guns: These are fast-firing weapons that excel in close-range combat and mobility. Some of the best submachine guns are QQ9, QXR, RUS-79U, MSMC, and PDW-57.</li>
-<li>Sniper Rifles: These are powerful weapons that can kill enemies with one shot from long distances. Some of the best sniper rifles are DL Q33, Locus, Arctic .50, Outlaw, and M21 EBR.</li>
-<li>Shotguns: These are devastating weapons that can deal massive damage in close-range combat. Some of the best shotguns are KRM-262, Echo, BY15, HS0405, and Striker.</li>
-<li>Pistols: These are secondary weapons that can be used as a backup or a finisher. Some of the best pistols are MW11, J358, .50 GS, Renetti, and AGR 556.</li>
-<li>Loadouts: These are combinations of weapons, attachments, perks, grenades, and operator skills that suit your playstyle and strategy. You can create up to 10 custom loadouts and switch between them during the game. You can also use the default loadouts or the recommended loadouts provided by the game.</li>
-</ul>
- <h3>Q4. How can I improve my skills and performance in Call of Duty Mobile?</h3>
-<p>A4. There are several tips and tricks that can help you improve your skills and performance in Call of Duty Mobile, such as:</p>
-<ul>
-<li>Practice: The best way to improve your skills is to practice regularly and learn from your mistakes. You can play different game modes and maps, try different weapons and loadouts, watch replays and tutorials, and challenge yourself with different goals and objectives.</li>
-<li>Learn: The more you know about the game, the better you can play it. You can learn about the game's mechanics, features, modes, maps, weapons, items, operators, skills, perks, etc. You can also learn from other players, streamers, youtubers, guides, forums, etc.</li>
-<li>Adjust: The more you adapt to the game, the better you can play it. You can adjust your settings, controls, sensitivity, layout, graphics, sound, etc. to suit your device and preference. You can also adjust your strategy, tactics, loadouts, etc. to suit the game mode, map, situation, etc.</li>
-<li>Communicate: The more you cooperate with your team, the better you can play it. You can communicate with your teammates using voice or text chat, ping system, gestures, etc. You can also coordinate your actions, roles, objectives, etc. with your teammates.</li>
-<li>Enjoy: The more you have fun with the game, the better you can play it. You can play the game casually or competitively, solo or with friends, online or offline, etc. You can also try new things, experiment with different options, and challenge yourself with different modes.</li>
-</ul>
- <h3>Q5. Where can I find more tips and tricks for Call of Duty Mobile?</h3>
-<p>A5. There are many sources where you can find more tips and tricks for Call of Duty Mobile, such as:</p>
-<ul>
-<li>The official website and social media accounts of Call of Duty Mobile that provide news, updates, events, announcements, etc.</li>
-<li>The in-game community section that provides forums, clans, leaderboards, feedbacks, support, etc.</li>
-<li>The online community platforms such as Reddit, Discord, Facebook, Twitter, Instagram, YouTube, Twitch, etc. that provide discussions, reviews, guides, videos, streams, etc.</li>
-<li>The online gaming websites and magazines that provide articles, blogs, podcasts, etc.</li>
-<li>The online gaming experts and influencers that provide tips, tricks, advice, recommendations, etc.</li>
-</ul>
- <p>I hope this article has helped you to understand what Call of Duty Mobile Unlimited CP Mod APK is and what you need to know about it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/APK Evozi How to Save Time and Data with the Fastest and Easiest APK Downloader.md

@@ -1,92 +0,0 @@
-<br />
-<h1>Apk Evozi: A Guide to Downloading Android Apps and Games</h1>
- <p>Do you want to download Android apps and games that are not available on Google Play? Do you want to save bandwidth and storage space by downloading only the APK file? Do you want to update your apps and games faster than waiting for Google Play? Do you want to avoid ads and in-app purchases by downloading modded or hacked versions of apps and games?</p>
- <p>If you answered yes to any of these questions, then you need to know about Apk Evozi, a website that lets you download any Android app or game from Google Play as an APK file. In this article, we will explain what Apk Evozi is, how it works, why you should use it, how to use it, and what are the benefits of using it. Let's get started!</p>
-<h2>apk evozi</h2><br /><p><b><b>Download</b> ===> <a href="https://jinyurl.com/2uNUv6">https://jinyurl.com/2uNUv6</a></b></p><br /><br />
- <h2>What is Apk Evozi?</h2>
- <p>Apk Evozi is a website that allows you to download any Android app or game from Google Play as an APK file. An APK file is the installation package of an Android app or game, which contains all the files and data needed to run it on your device. By downloading the APK file, you can install the app or game without using Google Play.</p>
- <p>Apk Evozi is developed by Evozi, a team of developers who create innovative apps for Android devices. Some of their popular apps include Network Speed, Internet Speed Meter, Device ID, and App Downloader. You can find more about them on their [Google Play page](^1^).</p>
- <h3>How does Apk Evozi work?</h3>
- <p>Apk Evozi works by using a script that fetches the APK file from Google Play servers and generates a download link for you. The script is updated regularly to ensure that it works with the latest versions of apps and games. The website also has a database of over 40 million APK files that you can browse and download.</p>
- <h3>Why use Apk Evozi?</h3>
- <p>There are many reasons why you might want to use Apk Evozi to download Android apps and games. Here are some of them:</p>
-<p>apk evozi downloader<br />
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- <ul>
-<li>You want to access apps and games that are not available in your region or device. For example, some apps and games are restricted to certain countries or devices due to licensing issues or compatibility issues. By using Apk Evozi, you can bypass these restrictions and download any app or game you want.</li>
-<li>You want to save bandwidth and storage space by downloading only the APK file. For example, some apps and games are very large in size and take a lot of time and data to download from Google Play. By using Apk Evozi, you can download only the APK file, which is usually much smaller than the full app or game. You can then transfer the APK file to your device using a USB cable or a cloud service and install it offline.</li>
-<li>You want to update your apps and games faster than waiting for Google Play. For example, some apps and games take a long time to receive updates from Google Play due to various reasons. By using Apk Evozi, you can download the latest version of any app or game as soon as it is released on Google Play.</li>
-<li>You want to avoid ads and in-app purchases by downloading modded or hacked versions of apps and games <h2>What are the benefits of using Apk Evozi?</h2>
- <p>By using Apk Evozi, you can enjoy many benefits that Google Play does not offer. Here are some of them:</p>
- <h3>Access to apps and games that are not available in your region or device</h3>
- <p>As we mentioned before, some apps and games are restricted to certain countries or devices due to licensing issues or compatibility issues. This can be frustrating if you want to try out a new app or game that is popular or useful in another region or device. By using Apk Evozi, you can download any app or game from any region or device and install it on your own device. For example, you can download the app Spotify, which is not available in some countries, or the game PUBG Mobile, which is not compatible with some devices.</p>
- <h3>Save bandwidth and storage space by downloading only the APK file</h3>
- <p>Another benefit of using Apk Evozi is that you can save bandwidth and storage space by downloading only the APK file. Some apps and games are very large in size and take a lot of time and data to download from Google Play. For example, the game Asphalt 9: Legends has a size of over 2 GB and requires an additional 1.5 GB of data to download after installation. By using Apk Evozi, you can download only the APK file, which is usually much smaller than the full app or game. For example, the APK file of Asphalt 9: Legends has a size of only 99 MB. You can then transfer the APK file to your device using a USB cable or a cloud service and install it offline. This way, you can save bandwidth and storage space on your device.</p>
- <h3>Update your apps and games faster than waiting for Google Play</h3>
- <p>A third benefit of using Apk Evozi is that you can update your apps and games faster than waiting for Google Play. Some apps and games take a long time to receive updates from Google Play due to various reasons. For example, some developers release updates in batches or stages to test their stability and performance before rolling them out to all users. Some users may also experience delays or errors in receiving updates from Google Play due to network issues or device settings. By using Apk Evozi, you can download the latest version of any app or game as soon as it is released on Google Play. You can also check the version history of any app or game on the Apk Evozi website to see if there are any new updates available.</p>
- <h3>Avoid ads and in-app purchases by downloading modded or hacked versions of apps and games</h3>
- <p>A fourth benefit of using Apk Evozi is that you can avoid ads and in-app purchases by downloading modded or hacked versions of apps and games. Some apps and games have annoying ads or require you to pay for extra features or content. For example, some apps have banner ads or pop-up ads that interrupt your experience or consume your data. Some games have in-app purchases that make you spend real money to unlock items, levels, characters, etc. By using Apk Evozi, you can download modded or hacked versions of apps and games that remove the ads or unlock the premium features or content for free. For example, you can download the modded version of Spotify that gives you unlimited skips, offline mode, no ads, etc. You can also download the hacked version of PUBG Mobile that gives you unlimited health, ammo, coins, etc. However, you should be careful when downloading modded or hacked versions of apps and games as they may contain malware or viruses that can harm your device or account.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Apk Evozi is a website that lets you download any Android app or game from Google Play as an APK file. By using Apk Evozi, you can access apps and games that are not available in your region or device, save bandwidth and storage space by downloading only the APK file, update your apps and games faster than waiting for Google Play, and avoid ads and in-app purchases by downloading modded or hacked versions of apps and games. To use Apk Evozi, you just need to find the app or game you want to download, copy its Google Play URL, paste it into the Apk Evozi downloader, click on the "Generate Download Link" button, and download the APK file and install it on your device.</p>
- <p>If you are looking for a way to download Android apps and games without using Google Play, then Apk Evozi is a great option for you. Try it out today and enjoy the benefits of downloading APK files!</p>
- <h2>FAQs</h2>
- <ul>
-<li><b>Q: Is Apk Evozi safe to use?</b></li>
-<li>A A: Apk Evozi is generally safe to use as it fetches the APK files from Google Play servers and does not modify them. However, you should always scan the APK files with a reliable antivirus software before installing them on your device. You should also be careful when downloading modded or hacked versions of apps and games as they may contain malware or viruses that can harm your device or account.</li>
- <li><b>Q: Is Apk Evozi legal to use?</b></li>
-<li>A: Apk Evozi is legal to use as long as you do not violate the terms and conditions of Google Play or the app or game developers. You should only download apps and games that you have purchased or are free to use. You should also respect the intellectual property rights of the app or game developers and not distribute or share the APK files without their permission.</li>
- <li><b>Q: Is Apk Evozi compatible with all Android devices?</b></li>
-<li>A: Apk Evozi is compatible with most Android devices that run on Android 4.0 or higher. However, some apps and games may not work properly on some devices due to hardware or software limitations. You should always check the compatibility and requirements of the app or game before downloading and installing it on your device.</li>
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- <li><b>Q: What are some alternatives to Apk Evozi?</b></li>
-<li>A: There are many other websites and apps that let you download APK files from Google Play or other sources. Some of them are APKPure, APKMirror, Aptoide, Uptodown, and Appvn. However, you should always be careful when using these websites and apps as they may not be as safe or reliable as Apk Evozi. You should always scan the APK files with a reliable antivirus software before installing them on your device.</li>
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spaces/AIConsultant/MusicGen/audiocraft/quantization/vq.py

@@ -1,115 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-
-import torch
-
-from .base import BaseQuantizer, QuantizedResult
-from .core_vq import ResidualVectorQuantization
-
-
-class ResidualVectorQuantizer(BaseQuantizer):
-    """Residual Vector Quantizer.
-
-    Args:
-        dimension (int): Dimension of the codebooks.
-        n_q (int): Number of residual vector quantizers used.
-        q_dropout (bool): Random quantizer drop out at train time.
-        bins (int): Codebook size.
-        decay (float): Decay for exponential moving average over the codebooks.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
-            for orthogonal regularization.
-    """
-    def __init__(
-        self,
-        dimension: int = 256,
-        n_q: int = 8,
-        q_dropout: bool = False,
-        bins: int = 1024,
-        decay: float = 0.99,
-        kmeans_init: bool = True,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        self.max_n_q = n_q
-        self.n_q = n_q
-        self.q_dropout = q_dropout
-        self.dimension = dimension
-        self.bins = bins
-        self.decay = decay
-        self.kmeans_init = kmeans_init
-        self.kmeans_iters = kmeans_iters
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-        self.vq = ResidualVectorQuantization(
-            dim=self.dimension,
-            codebook_size=self.bins,
-            num_quantizers=self.n_q,
-            decay=self.decay,
-            kmeans_init=self.kmeans_init,
-            kmeans_iters=self.kmeans_iters,
-            threshold_ema_dead_code=self.threshold_ema_dead_code,
-            orthogonal_reg_weight=self.orthogonal_reg_weight,
-            orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only,
-            orthogonal_reg_max_codes=self.orthogonal_reg_max_codes,
-            channels_last=False
-        )
-
-    def forward(self, x: torch.Tensor, frame_rate: int):
-        n_q = self.n_q
-        if self.training and self.q_dropout:
-            n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
-        bw_per_q = math.log2(self.bins) * frame_rate / 1000
-        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        bw = torch.tensor(n_q * bw_per_q).to(x)
-        return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Encode a given input tensor with the specified frame rate at the given bandwidth.
-        The RVQ encode method sets the appropriate number of quantizer to use
-        and returns indices for each quantizer.
-        """
-        n_q = self.n_q
-        codes = self.vq.encode(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        return codes
-
-    def decode(self, codes: torch.Tensor) -> torch.Tensor:
-        """Decode the given codes to the quantized representation."""
-        # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
-        codes = codes.transpose(0, 1)
-        quantized = self.vq.decode(codes)
-        return quantized
-
-    @property
-    def total_codebooks(self):
-        return self.max_n_q
-
-    @property
-    def num_codebooks(self):
-        return self.n_q
-
-    def set_num_codebooks(self, n: int):
-        assert n > 0 and n <= self.max_n_q
-        self.n_q = n

spaces/AIConsultant/MusicGen/tests/models/test_audiogen.py

@@ -1,53 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import pytest
-import torch
-
-from audiocraft.models import AudioGen
-
-
-class TestAudioGenModel:
-    def get_audiogen(self):
-        ag = AudioGen.get_pretrained(name='debug', device='cpu')
-        ag.set_generation_params(duration=2.0, extend_stride=2.)
-        return ag
-
-    def test_base(self):
-        ag = self.get_audiogen()
-        assert ag.frame_rate == 25
-        assert ag.sample_rate == 16000
-        assert ag.audio_channels == 1
-
-    def test_generate_continuation(self):
-        ag = self.get_audiogen()
-        prompt = torch.randn(3, 1, 16000)
-        wav = ag.generate_continuation(prompt, 16000)
-        assert list(wav.shape) == [3, 1, 32000]
-
-        prompt = torch.randn(2, 1, 16000)
-        wav = ag.generate_continuation(
-            prompt, 16000, ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 32000]
-
-        prompt = torch.randn(2, 1, 16000)
-        with pytest.raises(AssertionError):
-            wav = ag.generate_continuation(
-                prompt, 16000, ['youpi', 'lapin dort', 'one too many'])
-
-    def test_generate(self):
-        ag = self.get_audiogen()
-        wav = ag.generate(
-            ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 32000]
-
-    def test_generate_long(self):
-        ag = self.get_audiogen()
-        ag.max_duration = 3.
-        ag.set_generation_params(duration=4., extend_stride=2.)
-        wav = ag.generate(
-            ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 16000 * 4]

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/voc/yolov5_l-v61_fast_1xb32-50e_voc.py

@@ -1,25 +0,0 @@
-_base_ = './yolov5_s-v61_fast_1xb64-50e_voc.py'
-
-deepen_factor = 1.0
-widen_factor = 1.0
-train_batch_size_per_gpu = 32
-train_num_workers = 8
-
-load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007-096ef0eb.pth'  # noqa
-
-model = dict(
-    backbone=dict(
-        deepen_factor=deepen_factor,
-        widen_factor=widen_factor,
-    ),
-    neck=dict(
-        deepen_factor=deepen_factor,
-        widen_factor=widen_factor,
-    ),
-    bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
-
-train_dataloader = dict(
-    batch_size=train_batch_size_per_gpu, num_workers=train_num_workers)
-
-optim_wrapper = dict(
-    optimizer=dict(batch_size_per_gpu=train_batch_size_per_gpu))

spaces/Ababababababbababa/AraPoet/app.py

@@ -1,121 +0,0 @@
-# coding=utf8
-
-import json
-import torch
-import gradio as gr
-import pyarabic.araby as araby
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoConfig
-
-feature_names = [ 
-    "Title",
-    "Meter",
-    "Theme",
-    "Name",
-    "Era",
-    "Country",
-    "Type"
-]
-
-with open("./poet_names.json", 'r', encoding="utf-8") as fin:
-    poet_names = json.load(fin)
-
-def normalize_text(text):
-    text = araby.strip_tatweel(text)
-    return text
-
-def generate_poem(country, era, meter, theme, lang_type, poet, num_lines, num_poems, title):
-
-    num_poems = int(num_poems)
-    prompt = title
-    prompt = normalize_text(prompt)
-
-    features = [prompt, meter, theme, poet, era, country, lang_type]
-
-    prompt = ""
-    for name, feat in zip(feature_names, features):
-        prompt += f"{name}: {feat}; "
-    prompt += f"Length: {num_lines}; Poem:"
-
-    num_beams = 5
-    top_k = 50
-    top_p = 0.9
-    r_penalty = 5.
-
-    input_ids = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
-    print(f"> Running: {prompt} | {num_poems} Poems")
-    outputs = model.generate(input_ids=input_ids, 
-        min_length=32, 
-        max_length=256, 
-        do_sample=True,
-        top_k=top_k,
-        top_p=top_p,
-        repetition_penalty=r_penalty,
-        num_beams=num_beams, 
-        num_return_sequences=num_poems,
-        early_stopping=True
-    )
-
-    poems = []
-    print(f"> # of Outputs: {len(outputs)}")
-    for output in outputs:
-        raw = tokenizer.decode(output)
-        raw = raw.replace("<pad>", "").replace("</s>", "")
-        print("="*100)
-        print(raw)
-        print("="*100)
-        poems += ['\n'.join(raw.split("<s>"))]
-
-    return "\n\n".join(poems)   
-    
-meters = ['البسيط', 'التفعيله', 'الحداء', 'الخفيف', 'الدوبيت', 'الرجز', 'الرمل', 'السريع', 'السلسلة', 'الصخري', 'الطويل', 'الكامل', 'الكان كان', 'اللويحاني', 'المتدارك', 'المتقارب', 'المجتث', 'المديد', 'المسحوب', 'المضارع', 'المقتضب', 'المنسرح', 'المواليا', 'الموشح', 'الهجيني', 'الهزج', 'الوافر', 'بحر أحذ الكامل', 'بحر أحذ المديد', 'بحر أحذ الوافر', 'بحر البسيط', 'بحر التفعيله', 'بحر الخبب', 'بحر الخفيف', 'بحر الدوبيت', 'بحر الرجز', 'بحر الرمل', 'بحر السريع', 'بحر السلسلة', 'بحر الطويل', 'بحر القوما', 'بحر الكامل', 'بحر الكامل المقطوع', 'بحر المتدارك', 'بحر المتدارك المنهوك', 'بحر المتقارب', 'بحر المجتث', 'بحر المديد', 'بحر المضارع', 'بحر المقتضب', 'بحر المنسرح', 'بحر المواليا', 'بحر الهزج', 'بحر الوافر', 'بحر تفعيلة الرجز', 'بحر تفعيلة الرمل', 'بحر تفعيلة الكامل', 'بحر تفعيلة المتقارب', 'بحر مجزوء البسيط', 'بحر مجزوء الخفيف', 'بحر مجزوء الدوبيت', 'بحر مجزوء الرجز', 'بحر مجزوء الرمل', 'بحر مجزوء الرمل ', 'بحر مجزوء السريع', 'بحر مجزوء الطويل', 'بحر مجزوء الكامل', 'بحر مجزوء المتدارك', 'بحر مجزوء المتقارب', 'بحر مجزوء المجتث', 'بحر مجزوء المديد', 'بحر مجزوء المنسرح', 'بحر مجزوء المواليا', 'بحر مجزوء الهزج', 'بحر مجزوء الوافر', 'بحر مجزوء موشح', 'بحر مخلع البسيط', 'بحر مخلع الرجز', 'بحر مخلع الرمل', 'بحر مخلع السريع', 'بحر مخلع الكامل', 'بحر مخلع موشح', 'بحر مربع البسيط', 'بحر مربع الرجز', 'بحر مشطور الرجز', 'بحر مشطور السريع', 'بحر مشطور الطويل', 'بحر منهوك البسيط', 'بحر منهوك الرجز', 'بحر منهوك الكامل', 'بحر منهوك المنسرح', 'بحر موشح', 'بسيط', 'زجل', 'شعر التفعيلة', 'شعر حر', 'عامي', 'عدة أبحر', 'عموديه', 'مجزوء الخفيف', 'نثريه', 'None']
-themes = ['قصيدة اعتذار', 'قصيدة الاناشيد', 'قصيدة المعلقات', 'قصيدة حزينه', 'قصيدة دينية', 'قصيدة ذم', 'قصيدة رثاء', 'قصيدة رومنسيه', 'قصيدة سياسية', 'قصيدة شوق', 'قصيدة عامه', 'قصيدة عتاب', 'قصيدة غزل', 'قصيدة فراق', 'قصيدة قصيره', 'قصيدة مدح', 'قصيدة هجاء', 'قصيدة وطنيه', 'None']
-language_types = ['شعبي', 'عامي', 'فصحى', 'فصيح', '-', 'None']
-poet_era = ['العصر الأموي', 'العصر الأندلسي', 'العصر الأيوبي', 'العصر الإسلامي', 'العصر الجاهلي', 'العصر الحديث', 'العصر العباسي', 'العصر العثماني', 'العصر الفاطمي', 'العصر المملوكي', 'المخضرمين', 'المغرب والأندلس', 'عصر بين الدولتين', 'قبل الإسلام', 'None']
-countries = ['الأردن', 'الإمارات', 'البحرين', 'الجزائر', 'السعودية', 'السنغال', 'السودان', 'الصومال', 'العراق', 'الكويت', 'المغرب', 'اليمن', 'تونس', 'سوريا', 'سورية', 'عمان', 'فلسطين', 'قطر', 'لبنان', 'ليبيا', 'مصر', 'موريتانيا', 'None']
-
-tokenizer: AutoTokenizer = AutoTokenizer.from_pretrained("bkhmsi/arapoet-mt5", use_auth_token="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")
-model: AutoModelForSeq2SeqLM = AutoModelForSeq2SeqLM.from_pretrained("bkhmsi/arapoet-mt5", use_auth_token="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")
-model.eval()
-
-title = ""
-with gr.Blocks(title=title) as demo:
-    inputs = []
-
-    gr.Markdown(
-        """
-        # AraPoet: Controlled Arabic Poetry Generation
-        
-        The model hosted here is a finetuned version of [mT5-large](https://huggingface.co/google/mt5-large) (∼ 1.2B parameters) on the largest repository of Arabic poems, the [ashaar](https://huggingface.co/datasets/arbml/ashaar) dataset. 
-        The model can be conditioned on a set of attributes to control the style of the generated poem. 
-        Namely: the poet name, country, era, meter, theme, language type, title and the length of the poem.
-        You can start by clicking on one of the examples below or try your own input.
-        """
-    )
-
-    with gr.Row():
-        inputs += [gr.Dropdown(countries, label="Country", value="مصر")]
-        inputs += [gr.Dropdown(poet_era, label="Era", value="العصر الحديث")]
-    with gr.Row():
-        inputs += [gr.Dropdown(meters, label="Meter", value="بحر السريع")]
-        inputs += [gr.Dropdown(themes, label="Theme", value="قصيدة رومنسيه")]
-    with gr.Row():
-        inputs += [gr.Dropdown(language_types, label="Language Type", value="فصحى")]
-        inputs += [gr.Dropdown(poet_names, label="Poet", value="أحمد شوقي")]
-    with gr.Row():
-        inputs += [gr.Slider(2, 20, value=6, step=1, label="Number of Lines")]
-        inputs += [gr.Slider(1, 4, value=1, step=1, label="Number of Samples")]
-    with gr.Row():
-        inputs += [gr.Textbox(label="Title", value="إثن عنان القلب واسلم به")]
-
-    btn = gr.Button("Generate")
-    examples = gr.Examples(examples="./examples", inputs=inputs)
-    btn.click(generate_poem, inputs, gr.TextArea(label="Generation"))
-
-
-    gr.Markdown(
-        """
-        Checkout our [AraPoet Preprint](https://github.com/BKHMSI/BKHMSI.github.io/blob/master/archive/resources/AraPoet.pdf) for more details about the model.
-        """
-    )
-
-demo.launch()

spaces/AbandonedMuse/UnlimitedMusicGen/Dockerfile

@@ -1,26 +0,0 @@
-FROM nvidia/cuda:11.8.0-base-ubuntu22.04
-
-ENV DEBIAN_FRONTEND=noninteractive \
-    PYTHONUNBUFFERED=1 \
-    PYTHONIOENCODING=UTF-8
-RUN --mount=type=cache,target=/var/cache/apt --mount=type=cache,target=/var/lib/apt apt update &&\
-    apt install -y \
-    wget \
-    git \
-    pkg-config \
-    python3 \
-    python3-pip \
-    python-is-python3 \
-    ffmpeg \
-    libnvrtc11.2 \
-    libtcmalloc-minimal4
-
-RUN useradd -m -u 1000 ac
-RUN --mount=type=cache,target=/root/.cache python -m pip install --upgrade pip wheel
-ENV TORCH_COMMAND="pip install torch==2.0.1+cu118 torchaudio --extra-index-url https://download.pytorch.org/whl/cu118"
-RUN --mount=type=cache,target=/root/.cache python -m $TORCH_COMMAND
-RUN ln -s /usr/lib/x86_64-linux-gnu/libnvrtc.so.11.2 /usr/lib/x86_64-linux-gnu/libnvrtc.so
-USER 1000
-RUN mkdir ~/.cache
-RUN --mount=type=cache,target=/home/ac/.cache --mount=source=.,target=/home/ac/audiocraft python -m pip install -r /home/ac/audiocraft/requirements.txt
-WORKDIR /home/ac/audiocraft

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/deprecated/__init__.py

@@ -1,14 +0,0 @@
-from .AiService     import AiService
-from .CodeLinkAva   import CodeLinkAva
-from .DfeHub        import DfeHub
-from .EasyChat      import EasyChat
-from .Forefront     import Forefront
-from .GetGpt        import GetGpt
-from .Opchatgpts    import Opchatgpts
-from .Lockchat      import Lockchat
-from .Wewordle      import Wewordle
-from .Equing        import Equing
-from .Wuguokai      import Wuguokai
-from .V50           import V50
-from .FastGpt       import FastGpt
-from .ChatgptLogin  import ChatgptLogin

spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/role_assigner/__init__.py

@@ -1,6 +0,0 @@
-from agentverse.registry import Registry
-
-role_assigner_registry = Registry(name="RoleAssignerRegistry")
-
-from .base import BaseRoleAssigner
-from .role_description import DescriptionAssigner

spaces/AgentVerse/agentVerse/agentverse/utils.py

@@ -1,50 +0,0 @@
-from typing import NamedTuple, Union
-from enum import Enum
-
-import abc
-
-
-class AgentAction(NamedTuple):
-    """Agent's action to take."""
-
-    tool: str
-    tool_input: Union[str, dict]
-    log: str
-
-
-class AgentFinish(NamedTuple):
-    """Agent's return value."""
-
-    return_values: dict
-    log: str
-
-
-class AgentCriticism(NamedTuple):
-    """Agent's criticism."""
-
-    is_agree: bool
-    criticism: str
-    sender_agent: object = None
-
-
-class AGENT_TYPES(Enum):
-    ROLE_ASSIGNMENT = 0
-    SOLVER = 1
-    CRITIC = 2
-    EXECUTION = 3
-    EVALUATION = 4
-    MANAGER = 5
-
-
-class Singleton(abc.ABCMeta, type):
-    """
-    Singleton metaclass for ensuring only one instance of a class.
-    """
-
-    _instances = {}
-
-    def __call__(cls, *args, **kwargs):
-        """Call method for the singleton metaclass."""
-        if cls not in cls._instances:
-            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
-        return cls._instances[cls]

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/puff/Puff.d.ts

@@ -1,2 +0,0 @@
-import Base from '../base/Base';
-export default class Puff extends Base { }

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/GetEndPoint.js

@@ -1,27 +0,0 @@
-import GetThumbAlignPoint from './GetThumbAlignPoint.js';
-
-const AlignRight = Phaser.Display.Align.RIGHT_CENTER;
-const AlignBottom = Phaser.Display.Align.BOTTOM_CENTER;
-
-var GetEndoint = function (out) {
-    if (out === undefined) {
-        out = tmpPoint;
-    }
-    if (this.childrenMap.thumb) {
-        var align = (this.orientation === 0) ? AlignRight : AlignBottom;
-        GetThumbAlignPoint.call(this, align, out);
-    } else {
-        if (this.orientation === 0) {
-            out.x = this.innerRight - 1; // Add 1 pixel margin
-            out.y = this.centerY;
-        } else {
-            out.x = this.centerX;
-            out.y = this.innerBottom - 1; // Add 1 pixel margin
-        }
-    }
-    return out;
-}
-
-var tmpPoint = {};
-
-export default GetEndoint;

spaces/Aki004/herta-so-vits/demo.py

@@ -1,30 +0,0 @@
-import edge_tts
-import asyncio
-import librosa
-import soundfile
-import io
-
-from inference.infer_tool import Svc
-
-TEXT = "私はヘルタ。今は忙しいから、リモート人形のオート返答機能に任せる。こんにちは、こんにちは、ごきげんよう、良い日になりますように。それじゃ"
-VOICE = "ja-JP-NanamiNeural"
-OUTPUT_FILE = "test.mp3"
-
-asyncio.run(edge_tts.Communicate(TEXT, VOICE).save(OUTPUT_FILE))
-audio, sr = librosa.load(OUTPUT_FILE, sr=16000, mono=True)
-raw_path = io.BytesIO()
-soundfile.write(raw_path, audio, 16000, format="wav")
-raw_path.seek(0)
-print('checkpoint 1')
-
-model = Svc(fr"Herta-Svc/G_10000.pth", f"Herta-Svc/config.json", device = 'cpu')
-print('checkpoint 2')
-
-out_audio, out_sr = model.infer('speaker0', 0, raw_path,
-                                       auto_predict_f0 = True,
-                                       )
-print('checkpoint 3')
-
-soundfile.write('out_audio.wav', out_audio.cpu().numpy(), 44100)
-
-print("done")

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/rearrange_speaker.py

@@ -1,37 +0,0 @@
-import torch
-import argparse
-import json
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_dir", type=str, default="./OUTPUT_MODEL/G_latest.pth")
-    parser.add_argument("--config_dir", type=str, default="./configs/modified_finetune_speaker.json")
-    args = parser.parse_args()
-
-    model_sd = torch.load(args.model_dir, map_location='cpu')
-    with open(args.config_dir, 'r', encoding='utf-8') as f:
-        hps = json.load(f)
-
-    valid_speakers = list(hps['speakers'].keys())
-    if hps['data']['n_speakers'] > len(valid_speakers):
-        new_emb_g = torch.zeros([len(valid_speakers), 256])
-        old_emb_g = model_sd['model']['emb_g.weight']
-        for i, speaker in enumerate(valid_speakers):
-            new_emb_g[i, :] = old_emb_g[hps['speakers'][speaker], :]
-            hps['speakers'][speaker] = i
-        hps['data']['n_speakers'] = len(valid_speakers)
-        model_sd['model']['emb_g.weight'] = new_emb_g
-        with open("./finetune_speaker.json", 'w', encoding='utf-8') as f:
-            json.dump(hps, f, indent=2)
-        torch.save(model_sd, "./G_latest.pth")
-    else:
-        with open("./finetune_speaker.json", 'w', encoding='utf-8') as f:
-            json.dump(hps, f, indent=2)
-        torch.save(model_sd, "./G_latest.pth")
-    # save another config file copy in MoeGoe format
-    hps['speakers'] = valid_speakers
-    with open("./moegoe_config.json", 'w', encoding='utf-8') as f:
-        json.dump(hps, f, indent=2)
-
-
-

spaces/Alex123aaa/1234/README.md

@@ -1,13 +0,0 @@
----
-title: 1234
-emoji: 🌍
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.45.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ameaou/academic-chatgpt3.1/crazy_functions/总结word文档.py

@@ -1,127 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, os
-    # pip install python-docx 用于docx格式，跨平台
-    # pip install pywin32 用于doc格式，仅支持Win平台
-    for index, fp in enumerate(file_manifest):
-        if fp.split(".")[-1] == "docx":
-            from docx import Document
-            doc = Document(fp)
-            file_content = "\n".join([para.text for para in doc.paragraphs])
-        else:
-            import win32com.client
-            word = win32com.client.Dispatch("Word.Application")
-            word.visible = False
-            # 打开文件
-            print('fp', os.getcwd())
-            doc = word.Documents.Open(os.getcwd() + '/' + fp)
-            # file_content = doc.Content.Text
-            doc = word.ActiveDocument
-            file_content = doc.Range().Text
-            doc.Close()
-            word.Quit()
-
-        print(file_content)
-        # private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
-        max_token = model_info[llm_kwargs['llm_model']]['max_token']
-        TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
-        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=file_content,  
-            get_token_fn=model_info[llm_kwargs['llm_model']]['token_cnt'], 
-            limit=TOKEN_LIMIT_PER_FRAGMENT
-        )
-        this_paper_history = []
-        for i, paper_frag in enumerate(paper_fragments):
-            i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'
-            i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say_show_user, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=[],
-                sys_prompt="总结文章。"
-            )
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.extend([i_say_show_user,gpt_say])
-            this_paper_history.extend([i_say_show_user,gpt_say])
-
-        # 已经对该文章的所有片段总结完毕，如果文章被切分了，
-        if len(paper_fragments) > 1:
-            i_say = f"根据以上的对话，总结文章{os.path.abspath(fp)}的主要内容。"
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=this_paper_history,
-                sys_prompt="总结文章。"
-            )
-
-            history.extend([i_say,gpt_say])
-            this_paper_history.extend([i_say,gpt_say])
-
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    res = write_results_to_file(history)
-    chatbot.append(("所有文件都总结完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-@CatchException
-def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结Word文档。函数插件贡献者: JasonGuo1"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        from docx import Document
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    if txt.endswith('.docx') or txt.endswith('.doc'):
-        file_manifest = [txt]
-    else:
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \
-                        [f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -1,540 +0,0 @@
-import inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import CLIPImageProcessor, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import deprecate, logging
-from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def preprocess(image):
-    w, h = image.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    return 2.0 * image - 1.0
-
-
-def preprocess_mask(mask, scale_factor=8):
-    mask = mask.convert("L")
-    w, h = mask.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL.Image.NEAREST)
-    mask = np.array(mask).astype(np.float32) / 255.0
-    mask = np.tile(mask, (4, 1, 1))
-    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
-    mask = 1 - mask  # repaint white, keep black
-    return mask
-
-
-class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
-    r"""
-    Pipeline for text-guided image inpainting using Stable Diffusion. This is a *legacy feature* for Onnx pipelines to
-    provide compatibility with StableDiffusionInpaintPipelineLegacy and may be removed in the future.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-    _is_onnx = True
-
-    vae_encoder: OnnxRuntimeModel
-    vae_decoder: OnnxRuntimeModel
-    text_encoder: OnnxRuntimeModel
-    tokenizer: CLIPTokenizer
-    unet: OnnxRuntimeModel
-    scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
-    safety_checker: OnnxRuntimeModel
-    feature_extractor: CLIPImageProcessor
-
-    def __init__(
-        self,
-        vae_encoder: OnnxRuntimeModel,
-        vae_decoder: OnnxRuntimeModel,
-        text_encoder: OnnxRuntimeModel,
-        tokenizer: CLIPTokenizer,
-        unet: OnnxRuntimeModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: OnnxRuntimeModel,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        self.register_modules(
-            vae_encoder=vae_encoder,
-            vae_decoder=vae_decoder,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        num_images_per_prompt: Optional[int],
-        do_classifier_free_guidance: bool,
-        negative_prompt: Optional[str],
-        prompt_embeds: Optional[np.ndarray] = None,
-        negative_prompt_embeds: Optional[np.ndarray] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                prompt to be encoded
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-        """
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # get prompt text embeddings
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="np",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
-
-            if not np.array_equal(text_input_ids, untruncated_ids):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
-
-        prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt] * batch_size
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="np",
-            )
-            negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
-
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    def check_inputs(
-        self,
-        prompt,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[np.ndarray, PIL.Image.Image] = None,
-        mask_image: Union[np.ndarray, PIL.Image.Image] = None,
-        strength: float = 0.8,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        generator: Optional[np.random.RandomState] = None,
-        prompt_embeds: Optional[np.ndarray] = None,
-        negative_prompt_embeds: Optional[np.ndarray] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
-        callback_steps: int = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`nd.ndarray` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process. This is the image whose masked region will be inpainted.
-            mask_image (`nd.ndarray` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
-                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
-                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.uu
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter will be modulated by `strength`.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (?) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`np.random.RandomState`, *optional*):
-                A np.random.RandomState to make generation deterministic.
-            prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-
-        # check inputs. Raise error if not correct
-        self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
-
-        # define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-
-        if generator is None:
-            generator = np.random
-
-        # set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        if isinstance(image, PIL.Image.Image):
-            image = preprocess(image)
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-        )
-
-        latents_dtype = prompt_embeds.dtype
-        image = image.astype(latents_dtype)
-
-        # encode the init image into latents and scale the latents
-        init_latents = self.vae_encoder(sample=image)[0]
-        init_latents = 0.18215 * init_latents
-
-        # Expand init_latents for batch_size and num_images_per_prompt
-        init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)
-        init_latents_orig = init_latents
-
-        # preprocess mask
-        if not isinstance(mask_image, np.ndarray):
-            mask_image = preprocess_mask(mask_image, 8)
-        mask_image = mask_image.astype(latents_dtype)
-        mask = np.concatenate([mask_image] * num_images_per_prompt, axis=0)
-
-        # check sizes
-        if not mask.shape == init_latents.shape:
-            raise ValueError("The mask and image should be the same size!")
-
-        # get the original timestep using init_timestep
-        offset = self.scheduler.config.get("steps_offset", 0)
-        init_timestep = int(num_inference_steps * strength) + offset
-        init_timestep = min(init_timestep, num_inference_steps)
-
-        timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
-        timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)
-
-        # add noise to latents using the timesteps
-        noise = generator.randn(*init_latents.shape).astype(latents_dtype)
-        init_latents = self.scheduler.add_noise(
-            torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)
-        )
-        init_latents = init_latents.numpy()
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (?) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to ? in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        latents = init_latents
-
-        t_start = max(num_inference_steps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:].numpy()
-        timestep_dtype = next(
-            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
-        )
-        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
-
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            timestep = np.array([t], dtype=timestep_dtype)
-            noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[
-                0
-            ]
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(
-                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
-            ).prev_sample
-
-            latents = latents.numpy()
-
-            init_latents_proper = self.scheduler.add_noise(
-                torch.from_numpy(init_latents_orig), torch.from_numpy(noise), torch.from_numpy(np.array([t]))
-            )
-
-            init_latents_proper = init_latents_proper.numpy()
-
-            latents = (init_latents_proper * mask) + (latents * (1 - mask))
-
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        latents = 1 / 0.18215 * latents
-        # image = self.vae_decoder(latent_sample=latents)[0]
-        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
-        image = np.concatenate(
-            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
-        )
-
-        image = np.clip(image / 2 + 0.5, 0, 1)
-        image = image.transpose((0, 2, 3, 1))
-
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(
-                self.numpy_to_pil(image), return_tensors="np"
-            ).pixel_values.astype(image.dtype)
-            # There will throw an error if use safety_checker batchsize>1
-            images, has_nsfw_concept = [], []
-            for i in range(image.shape[0]):
-                image_i, has_nsfw_concept_i = self.safety_checker(
-                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
-                )
-                images.append(image_i)
-                has_nsfw_concept.append(has_nsfw_concept_i[0])
-            image = np.concatenate(images)
-        else:
-            has_nsfw_concept = None
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/Andy1621/uniformer_image_detection/mmdet/core/anchor/utils.py

@@ -1,71 +0,0 @@
-import torch
-
-
-def images_to_levels(target, num_levels):
-    """Convert targets by image to targets by feature level.
-
-    [target_img0, target_img1] -> [target_level0, target_level1, ...]
-    """
-    target = torch.stack(target, 0)
-    level_targets = []
-    start = 0
-    for n in num_levels:
-        end = start + n
-        # level_targets.append(target[:, start:end].squeeze(0))
-        level_targets.append(target[:, start:end])
-        start = end
-    return level_targets
-
-
-def anchor_inside_flags(flat_anchors,
-                        valid_flags,
-                        img_shape,
-                        allowed_border=0):
-    """Check whether the anchors are inside the border.
-
-    Args:
-        flat_anchors (torch.Tensor): Flatten anchors, shape (n, 4).
-        valid_flags (torch.Tensor): An existing valid flags of anchors.
-        img_shape (tuple(int)): Shape of current image.
-        allowed_border (int, optional): The border to allow the valid anchor.
-            Defaults to 0.
-
-    Returns:
-        torch.Tensor: Flags indicating whether the anchors are inside a \
-            valid range.
-    """
-    img_h, img_w = img_shape[:2]
-    if allowed_border >= 0:
-        inside_flags = valid_flags & \
-            (flat_anchors[:, 0] >= -allowed_border) & \
-            (flat_anchors[:, 1] >= -allowed_border) & \
-            (flat_anchors[:, 2] < img_w + allowed_border) & \
-            (flat_anchors[:, 3] < img_h + allowed_border)
-    else:
-        inside_flags = valid_flags
-    return inside_flags
-
-
-def calc_region(bbox, ratio, featmap_size=None):
-    """Calculate a proportional bbox region.
-
-    The bbox center are fixed and the new h' and w' is h * ratio and w * ratio.
-
-    Args:
-        bbox (Tensor): Bboxes to calculate regions, shape (n, 4).
-        ratio (float): Ratio of the output region.
-        featmap_size (tuple): Feature map size used for clipping the boundary.
-
-    Returns:
-        tuple: x1, y1, x2, y2
-    """
-    x1 = torch.round((1 - ratio) * bbox[0] + ratio * bbox[2]).long()
-    y1 = torch.round((1 - ratio) * bbox[1] + ratio * bbox[3]).long()
-    x2 = torch.round(ratio * bbox[0] + (1 - ratio) * bbox[2]).long()
-    y2 = torch.round(ratio * bbox[1] + (1 - ratio) * bbox[3]).long()
-    if featmap_size is not None:
-        x1 = x1.clamp(min=0, max=featmap_size[1])
-        y1 = y1.clamp(min=0, max=featmap_size[0])
-        x2 = x2.clamp(min=0, max=featmap_size[1])
-        y2 = y2.clamp(min=0, max=featmap_size[0])
-    return (x1, y1, x2, y2)

spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/pisa_loss.py

@@ -1,183 +0,0 @@
-import mmcv
-import torch
-
-from mmdet.core import bbox_overlaps
-
-
-@mmcv.jit(derivate=True, coderize=True)
-def isr_p(cls_score,
-          bbox_pred,
-          bbox_targets,
-          rois,
-          sampling_results,
-          loss_cls,
-          bbox_coder,
-          k=2,
-          bias=0,
-          num_class=80):
-    """Importance-based Sample Reweighting (ISR_P), positive part.
-
-    Args:
-        cls_score (Tensor): Predicted classification scores.
-        bbox_pred (Tensor): Predicted bbox deltas.
-        bbox_targets (tuple[Tensor]): A tuple of bbox targets, the are
-            labels, label_weights, bbox_targets, bbox_weights, respectively.
-        rois (Tensor): Anchors (single_stage) in shape (n, 4) or RoIs
-            (two_stage) in shape (n, 5).
-        sampling_results (obj): Sampling results.
-        loss_cls (func): Classification loss func of the head.
-        bbox_coder (obj): BBox coder of the head.
-        k (float): Power of the non-linear mapping.
-        bias (float): Shift of the non-linear mapping.
-        num_class (int): Number of classes, default: 80.
-
-    Return:
-        tuple([Tensor]): labels, imp_based_label_weights, bbox_targets,
-            bbox_target_weights
-    """
-
-    labels, label_weights, bbox_targets, bbox_weights = bbox_targets
-    pos_label_inds = ((labels >= 0) &
-                      (labels < num_class)).nonzero().reshape(-1)
-    pos_labels = labels[pos_label_inds]
-
-    # if no positive samples, return the original targets
-    num_pos = float(pos_label_inds.size(0))
-    if num_pos == 0:
-        return labels, label_weights, bbox_targets, bbox_weights
-
-    # merge pos_assigned_gt_inds of per image to a single tensor
-    gts = list()
-    last_max_gt = 0
-    for i in range(len(sampling_results)):
-        gt_i = sampling_results[i].pos_assigned_gt_inds
-        gts.append(gt_i + last_max_gt)
-        if len(gt_i) != 0:
-            last_max_gt = gt_i.max() + 1
-    gts = torch.cat(gts)
-    assert len(gts) == num_pos
-
-    cls_score = cls_score.detach()
-    bbox_pred = bbox_pred.detach()
-
-    # For single stage detectors, rois here indicate anchors, in shape (N, 4)
-    # For two stage detectors, rois are in shape (N, 5)
-    if rois.size(-1) == 5:
-        pos_rois = rois[pos_label_inds][:, 1:]
-    else:
-        pos_rois = rois[pos_label_inds]
-
-    if bbox_pred.size(-1) > 4:
-        bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
-        pos_delta_pred = bbox_pred[pos_label_inds, pos_labels].view(-1, 4)
-    else:
-        pos_delta_pred = bbox_pred[pos_label_inds].view(-1, 4)
-
-    # compute iou of the predicted bbox and the corresponding GT
-    pos_delta_target = bbox_targets[pos_label_inds].view(-1, 4)
-    pos_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_pred)
-    target_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_target)
-    ious = bbox_overlaps(pos_bbox_pred, target_bbox_pred, is_aligned=True)
-
-    pos_imp_weights = label_weights[pos_label_inds]
-    # Two steps to compute IoU-HLR. Samples are first sorted by IoU locally,
-    # then sorted again within the same-rank group
-    max_l_num = pos_labels.bincount().max()
-    for label in pos_labels.unique():
-        l_inds = (pos_labels == label).nonzero().view(-1)
-        l_gts = gts[l_inds]
-        for t in l_gts.unique():
-            t_inds = l_inds[l_gts == t]
-            t_ious = ious[t_inds]
-            _, t_iou_rank_idx = t_ious.sort(descending=True)
-            _, t_iou_rank = t_iou_rank_idx.sort()
-            ious[t_inds] += max_l_num - t_iou_rank.float()
-        l_ious = ious[l_inds]
-        _, l_iou_rank_idx = l_ious.sort(descending=True)
-        _, l_iou_rank = l_iou_rank_idx.sort()  # IoU-HLR
-        # linearly map HLR to label weights
-        pos_imp_weights[l_inds] *= (max_l_num - l_iou_rank.float()) / max_l_num
-
-    pos_imp_weights = (bias + pos_imp_weights * (1 - bias)).pow(k)
-
-    # normalize to make the new weighted loss value equal to the original loss
-    pos_loss_cls = loss_cls(
-        cls_score[pos_label_inds], pos_labels, reduction_override='none')
-    if pos_loss_cls.dim() > 1:
-        ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds][:,
-                                                                        None]
-        new_pos_loss_cls = pos_loss_cls * pos_imp_weights[:, None]
-    else:
-        ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds]
-        new_pos_loss_cls = pos_loss_cls * pos_imp_weights
-    pos_loss_cls_ratio = ori_pos_loss_cls.sum() / new_pos_loss_cls.sum()
-    pos_imp_weights = pos_imp_weights * pos_loss_cls_ratio
-    label_weights[pos_label_inds] = pos_imp_weights
-
-    bbox_targets = labels, label_weights, bbox_targets, bbox_weights
-    return bbox_targets
-
-
-@mmcv.jit(derivate=True, coderize=True)
-def carl_loss(cls_score,
-              labels,
-              bbox_pred,
-              bbox_targets,
-              loss_bbox,
-              k=1,
-              bias=0.2,
-              avg_factor=None,
-              sigmoid=False,
-              num_class=80):
-    """Classification-Aware Regression Loss (CARL).
-
-    Args:
-        cls_score (Tensor): Predicted classification scores.
-        labels (Tensor): Targets of classification.
-        bbox_pred (Tensor): Predicted bbox deltas.
-        bbox_targets (Tensor): Target of bbox regression.
-        loss_bbox (func): Regression loss func of the head.
-        bbox_coder (obj): BBox coder of the head.
-        k (float): Power of the non-linear mapping.
-        bias (float): Shift of the non-linear mapping.
-        avg_factor (int): Average factor used in regression loss.
-        sigmoid (bool): Activation of the classification score.
-        num_class (int): Number of classes, default: 80.
-
-    Return:
-        dict: CARL loss dict.
-    """
-    pos_label_inds = ((labels >= 0) &
-                      (labels < num_class)).nonzero().reshape(-1)
-    if pos_label_inds.numel() == 0:
-        return dict(loss_carl=cls_score.sum()[None] * 0.)
-    pos_labels = labels[pos_label_inds]
-
-    # multiply pos_cls_score with the corresponding bbox weight
-    # and remain gradient
-    if sigmoid:
-        pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels]
-    else:
-        pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels]
-    carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k)
-
-    # normalize carl_loss_weight to make its sum equal to num positive
-    num_pos = float(pos_cls_score.size(0))
-    weight_ratio = num_pos / carl_loss_weights.sum()
-    carl_loss_weights *= weight_ratio
-
-    if avg_factor is None:
-        avg_factor = bbox_targets.size(0)
-    # if is class agnostic, bbox pred is in shape (N, 4)
-    # otherwise, bbox pred is in shape (N, #classes, 4)
-    if bbox_pred.size(-1) > 4:
-        bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
-        pos_bbox_preds = bbox_pred[pos_label_inds, pos_labels]
-    else:
-        pos_bbox_preds = bbox_pred[pos_label_inds]
-    ori_loss_reg = loss_bbox(
-        pos_bbox_preds,
-        bbox_targets[pos_label_inds],
-        reduction_override='none') / avg_factor
-    loss_carl = (ori_loss_reg * carl_loss_weights[:, None]).sum()
-    return dict(loss_carl=loss_carl[None])

spaces/Andy1621/uniformer_video_demo/uniformer.py

@@ -1,379 +0,0 @@
-from collections import OrderedDict
-import torch
-import torch.nn as nn
-from functools import partial
-from timm.models.layers import trunc_normal_, DropPath, to_2tuple
-
-
-def conv_3xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
-    return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (2, stride, stride), (1, 0, 0), groups=groups)
-    
-def conv_1xnxn(inp, oup, kernel_size=3, stride=3, groups=1):
-    return nn.Conv3d(inp, oup, (1, kernel_size, kernel_size), (1, stride, stride), (0, 0, 0), groups=groups)
-
-def conv_3xnxn_std(inp, oup, kernel_size=3, stride=3, groups=1):
-    return nn.Conv3d(inp, oup, (3, kernel_size, kernel_size), (1, stride, stride), (1, 0, 0), groups=groups)
-
-def conv_1x1x1(inp, oup, groups=1):
-    return nn.Conv3d(inp, oup, (1, 1, 1), (1, 1, 1), (0, 0, 0), groups=groups)
-
-def conv_3x3x3(inp, oup, groups=1):
-    return nn.Conv3d(inp, oup, (3, 3, 3), (1, 1, 1), (1, 1, 1), groups=groups)
-
-def conv_5x5x5(inp, oup, groups=1):
-    return nn.Conv3d(inp, oup, (5, 5, 5), (1, 1, 1), (2, 2, 2), groups=groups)
-
-def bn_3d(dim):
-    return nn.BatchNorm3d(dim)
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class CMlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = conv_1x1x1(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = conv_1x1x1(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-    
-    
-class CBlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
-        self.norm1 = bn_3d(dim)
-        self.conv1 = conv_1x1x1(dim, dim, 1)
-        self.conv2 = conv_1x1x1(dim, dim, 1)
-        self.attn = conv_5x5x5(dim, dim, groups=dim)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = bn_3d(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x)))))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x   
-
-
-class SABlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        B, C, T, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = x + self.drop_path(self.attn(self.norm1(x)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        x = x.transpose(1, 2).reshape(B, C, T, H, W)
-        return x    
-
-
-class SplitSABlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = conv_3x3x3(dim, dim, groups=dim)
-        self.t_norm = norm_layer(dim)
-        self.t_attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        B, C, T, H, W = x.shape
-        attn = x.view(B, C, T, H * W).permute(0, 3, 2, 1).contiguous()
-        attn = attn.view(B * H * W, T, C)
-        attn = attn + self.drop_path(self.t_attn(self.t_norm(attn)))
-        attn = attn.view(B, H * W, T, C).permute(0, 2, 1, 3).contiguous()
-        attn = attn.view(B * T, H * W, C)
-        residual = x.view(B, C, T, H * W).permute(0, 2, 3, 1).contiguous()
-        residual = residual.view(B * T, H * W, C)
-        attn = residual + self.drop_path(self.attn(self.norm1(attn)))
-        attn = attn.view(B, T * H * W, C)
-        out = attn + self.drop_path(self.mlp(self.norm2(attn)))
-        out = out.transpose(1, 2).reshape(B, C, T, H, W)
-        return out
-
-
-class SpeicalPatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-        self.norm = nn.LayerNorm(embed_dim)
-        self.proj = conv_3xnxn(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
-
-    def forward(self, x):
-        B, C, T, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1], \
-        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x)
-        B, C, T, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = self.norm(x)
-        x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
-        return x
-    
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, std=False):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-        self.norm = nn.LayerNorm(embed_dim)
-        if std:
-            self.proj = conv_3xnxn_std(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
-        else:
-            self.proj = conv_1xnxn(in_chans, embed_dim, kernel_size=patch_size[0], stride=patch_size[0])
-
-    def forward(self, x):
-        B, C, T, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1], \
-        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x)
-        B, C, T, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = self.norm(x)
-        x = x.reshape(B, T, H, W, -1).permute(0, 4, 1, 2, 3).contiguous()
-        return x
-
-
-class Uniformer(nn.Module):
-    """ Vision Transformer
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
-        https://arxiv.org/abs/2010.11929
-    """
-    def __init__(self, depth=[5, 8, 20, 7], num_classes=400, img_size=224, in_chans=3, embed_dim=[64, 128, 320, 512],
-                 head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
-                 drop_rate=0.3, attn_drop_rate=0., drop_path_rate=0., norm_layer=None, split=False, std=False):
-        super().__init__()
-
-        self.num_classes = num_classes
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        norm_layer = partial(nn.LayerNorm, eps=1e-6) 
-        
-        self.patch_embed1 = SpeicalPatchEmbed(
-            img_size=img_size, patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0])
-        self.patch_embed2 = PatchEmbed(
-            img_size=img_size // 4, patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1], std=std)
-        self.patch_embed3 = PatchEmbed(
-            img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2], std=std)
-        self.patch_embed4 = PatchEmbed(
-            img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3], std=std)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depth))]  # stochastic depth decay rule
-        num_heads = [dim // head_dim for dim in embed_dim]
-        self.blocks1 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[0], num_heads=num_heads[0], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
-            for i in range(depth[0])])
-        self.blocks2 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]], norm_layer=norm_layer)
-            for i in range(depth[1])])
-        if split:
-            self.blocks3 = nn.ModuleList([
-                SplitSABlock(
-                    dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
-                for i in range(depth[2])])
-            self.blocks4 = nn.ModuleList([
-                SplitSABlock(
-                    dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
-            for i in range(depth[3])])
-        else:
-            self.blocks3 = nn.ModuleList([
-                SABlock(
-                    dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
-                for i in range(depth[2])])
-            self.blocks4 = nn.ModuleList([
-                SABlock(
-                    dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
-            for i in range(depth[3])])
-        self.norm = bn_3d(embed_dim[-1])
-        
-        # Representation layer
-        if representation_size:
-            self.num_features = representation_size
-            self.pre_logits = nn.Sequential(OrderedDict([
-                ('fc', nn.Linear(embed_dim, representation_size)),
-                ('act', nn.Tanh())
-            ]))
-        else:
-            self.pre_logits = nn.Identity()
-        
-        # Classifier head
-        self.head = nn.Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
-        
-        self.apply(self._init_weights)
-
-        for name, p in self.named_parameters():
-            # fill proj weight with 1 here to improve training dynamics. Otherwise temporal attention inputs
-            # are multiplied by 0*0, which is hard for the model to move out of.
-            if 't_attn.qkv.weight' in name:
-                nn.init.constant_(p, 0)
-            if 't_attn.qkv.bias' in name:
-                nn.init.constant_(p, 0)
-            if 't_attn.proj.weight' in name:
-                nn.init.constant_(p, 1)
-            if 't_attn.proj.bias' in name:
-                nn.init.constant_(p, 0)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed', 'cls_token'}
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-    def forward_features(self, x):
-        x = self.patch_embed1(x)
-        x = self.pos_drop(x)
-        for blk in self.blocks1:
-            x = blk(x)
-        x = self.patch_embed2(x)
-        for blk in self.blocks2:
-            x = blk(x)
-        x = self.patch_embed3(x)
-        for blk in self.blocks3:
-            x = blk(x)
-        x = self.patch_embed4(x)
-        for blk in self.blocks4:
-            x = blk(x)
-        x = self.norm(x)
-        x = self.pre_logits(x)
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = x.flatten(2).mean(-1)
-        x = self.head(x)
-        return x
-        
-
-def uniformer_small():
-    return Uniformer(
-        depth=[3, 4, 8, 3], embed_dim=[64, 128, 320, 512], 
-        head_dim=64, drop_rate=0.1)
-
-def uniformer_base():
-    return Uniformer(
-        depth=[5, 8, 20, 7], embed_dim=[64, 128, 320, 512], 
-        head_dim=64, drop_rate=0.3)

spaces/ArpitM/chat-llm-streaming/README.md

@@ -1,12 +0,0 @@
----
-title: Chat Llm Streaming
-emoji: 📊
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/syntax.py

@@ -1,950 +0,0 @@
-import os.path
-import platform
-import re
-import sys
-import textwrap
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import (
-    Any,
-    Dict,
-    Iterable,
-    List,
-    NamedTuple,
-    Optional,
-    Sequence,
-    Set,
-    Tuple,
-    Type,
-    Union,
-)
-
-from pip._vendor.pygments.lexer import Lexer
-from pip._vendor.pygments.lexers import get_lexer_by_name, guess_lexer_for_filename
-from pip._vendor.pygments.style import Style as PygmentsStyle
-from pip._vendor.pygments.styles import get_style_by_name
-from pip._vendor.pygments.token import (
-    Comment,
-    Error,
-    Generic,
-    Keyword,
-    Name,
-    Number,
-    Operator,
-    String,
-    Token,
-    Whitespace,
-)
-from pip._vendor.pygments.util import ClassNotFound
-
-from pip._vendor.rich.containers import Lines
-from pip._vendor.rich.padding import Padding, PaddingDimensions
-
-from ._loop import loop_first
-from .cells import cell_len
-from .color import Color, blend_rgb
-from .console import Console, ConsoleOptions, JustifyMethod, RenderResult
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment, Segments
-from .style import Style, StyleType
-from .text import Text
-
-TokenType = Tuple[str, ...]
-
-WINDOWS = platform.system() == "Windows"
-DEFAULT_THEME = "monokai"
-
-# The following styles are based on https://github.com/pygments/pygments/blob/master/pygments/formatters/terminal.py
-# A few modifications were made
-
-ANSI_LIGHT: Dict[TokenType, Style] = {
-    Token: Style(),
-    Whitespace: Style(color="white"),
-    Comment: Style(dim=True),
-    Comment.Preproc: Style(color="cyan"),
-    Keyword: Style(color="blue"),
-    Keyword.Type: Style(color="cyan"),
-    Operator.Word: Style(color="magenta"),
-    Name.Builtin: Style(color="cyan"),
-    Name.Function: Style(color="green"),
-    Name.Namespace: Style(color="cyan", underline=True),
-    Name.Class: Style(color="green", underline=True),
-    Name.Exception: Style(color="cyan"),
-    Name.Decorator: Style(color="magenta", bold=True),
-    Name.Variable: Style(color="red"),
-    Name.Constant: Style(color="red"),
-    Name.Attribute: Style(color="cyan"),
-    Name.Tag: Style(color="bright_blue"),
-    String: Style(color="yellow"),
-    Number: Style(color="blue"),
-    Generic.Deleted: Style(color="bright_red"),
-    Generic.Inserted: Style(color="green"),
-    Generic.Heading: Style(bold=True),
-    Generic.Subheading: Style(color="magenta", bold=True),
-    Generic.Prompt: Style(bold=True),
-    Generic.Error: Style(color="bright_red"),
-    Error: Style(color="red", underline=True),
-}
-
-ANSI_DARK: Dict[TokenType, Style] = {
-    Token: Style(),
-    Whitespace: Style(color="bright_black"),
-    Comment: Style(dim=True),
-    Comment.Preproc: Style(color="bright_cyan"),
-    Keyword: Style(color="bright_blue"),
-    Keyword.Type: Style(color="bright_cyan"),
-    Operator.Word: Style(color="bright_magenta"),
-    Name.Builtin: Style(color="bright_cyan"),
-    Name.Function: Style(color="bright_green"),
-    Name.Namespace: Style(color="bright_cyan", underline=True),
-    Name.Class: Style(color="bright_green", underline=True),
-    Name.Exception: Style(color="bright_cyan"),
-    Name.Decorator: Style(color="bright_magenta", bold=True),
-    Name.Variable: Style(color="bright_red"),
-    Name.Constant: Style(color="bright_red"),
-    Name.Attribute: Style(color="bright_cyan"),
-    Name.Tag: Style(color="bright_blue"),
-    String: Style(color="yellow"),
-    Number: Style(color="bright_blue"),
-    Generic.Deleted: Style(color="bright_red"),
-    Generic.Inserted: Style(color="bright_green"),
-    Generic.Heading: Style(bold=True),
-    Generic.Subheading: Style(color="bright_magenta", bold=True),
-    Generic.Prompt: Style(bold=True),
-    Generic.Error: Style(color="bright_red"),
-    Error: Style(color="red", underline=True),
-}
-
-RICH_SYNTAX_THEMES = {"ansi_light": ANSI_LIGHT, "ansi_dark": ANSI_DARK}
-NUMBERS_COLUMN_DEFAULT_PADDING = 2
-
-
-class SyntaxTheme(ABC):
-    """Base class for a syntax theme."""
-
-    @abstractmethod
-    def get_style_for_token(self, token_type: TokenType) -> Style:
-        """Get a style for a given Pygments token."""
-        raise NotImplementedError  # pragma: no cover
-
-    @abstractmethod
-    def get_background_style(self) -> Style:
-        """Get the background color."""
-        raise NotImplementedError  # pragma: no cover
-
-
-class PygmentsSyntaxTheme(SyntaxTheme):
-    """Syntax theme that delegates to Pygments theme."""
-
-    def __init__(self, theme: Union[str, Type[PygmentsStyle]]) -> None:
-        self._style_cache: Dict[TokenType, Style] = {}
-        if isinstance(theme, str):
-            try:
-                self._pygments_style_class = get_style_by_name(theme)
-            except ClassNotFound:
-                self._pygments_style_class = get_style_by_name("default")
-        else:
-            self._pygments_style_class = theme
-
-        self._background_color = self._pygments_style_class.background_color
-        self._background_style = Style(bgcolor=self._background_color)
-
-    def get_style_for_token(self, token_type: TokenType) -> Style:
-        """Get a style from a Pygments class."""
-        try:
-            return self._style_cache[token_type]
-        except KeyError:
-            try:
-                pygments_style = self._pygments_style_class.style_for_token(token_type)
-            except KeyError:
-                style = Style.null()
-            else:
-                color = pygments_style["color"]
-                bgcolor = pygments_style["bgcolor"]
-                style = Style(
-                    color="#" + color if color else "#000000",
-                    bgcolor="#" + bgcolor if bgcolor else self._background_color,
-                    bold=pygments_style["bold"],
-                    italic=pygments_style["italic"],
-                    underline=pygments_style["underline"],
-                )
-            self._style_cache[token_type] = style
-        return style
-
-    def get_background_style(self) -> Style:
-        return self._background_style
-
-
-class ANSISyntaxTheme(SyntaxTheme):
-    """Syntax theme to use standard colors."""
-
-    def __init__(self, style_map: Dict[TokenType, Style]) -> None:
-        self.style_map = style_map
-        self._missing_style = Style.null()
-        self._background_style = Style.null()
-        self._style_cache: Dict[TokenType, Style] = {}
-
-    def get_style_for_token(self, token_type: TokenType) -> Style:
-        """Look up style in the style map."""
-        try:
-            return self._style_cache[token_type]
-        except KeyError:
-            # Styles form a hierarchy
-            # We need to go from most to least specific
-            # e.g. ("foo", "bar", "baz") to ("foo", "bar")  to ("foo",)
-            get_style = self.style_map.get
-            token = tuple(token_type)
-            style = self._missing_style
-            while token:
-                _style = get_style(token)
-                if _style is not None:
-                    style = _style
-                    break
-                token = token[:-1]
-            self._style_cache[token_type] = style
-            return style
-
-    def get_background_style(self) -> Style:
-        return self._background_style
-
-
-SyntaxPosition = Tuple[int, int]
-
-
-class _SyntaxHighlightRange(NamedTuple):
-    """
-    A range to highlight in a Syntax object.
-    `start` and `end` are 2-integers tuples, where the first integer is the line number
-    (starting from 1) and the second integer is the column index (starting from 0).
-    """
-
-    style: StyleType
-    start: SyntaxPosition
-    end: SyntaxPosition
-
-
-class Syntax(JupyterMixin):
-    """Construct a Syntax object to render syntax highlighted code.
-
-    Args:
-        code (str): Code to highlight.
-        lexer (Lexer | str): Lexer to use (see https://pygments.org/docs/lexers/)
-        theme (str, optional): Color theme, aka Pygments style (see https://pygments.org/docs/styles/#getting-a-list-of-available-styles). Defaults to "monokai".
-        dedent (bool, optional): Enable stripping of initial whitespace. Defaults to False.
-        line_numbers (bool, optional): Enable rendering of line numbers. Defaults to False.
-        start_line (int, optional): Starting number for line numbers. Defaults to 1.
-        line_range (Tuple[int | None, int | None], optional): If given should be a tuple of the start and end line to render.
-            A value of None in the tuple indicates the range is open in that direction.
-        highlight_lines (Set[int]): A set of line numbers to highlight.
-        code_width: Width of code to render (not including line numbers), or ``None`` to use all available width.
-        tab_size (int, optional): Size of tabs. Defaults to 4.
-        word_wrap (bool, optional): Enable word wrapping.
-        background_color (str, optional): Optional background color, or None to use theme color. Defaults to None.
-        indent_guides (bool, optional): Show indent guides. Defaults to False.
-        padding (PaddingDimensions): Padding to apply around the syntax. Defaults to 0 (no padding).
-    """
-
-    _pygments_style_class: Type[PygmentsStyle]
-    _theme: SyntaxTheme
-
-    @classmethod
-    def get_theme(cls, name: Union[str, SyntaxTheme]) -> SyntaxTheme:
-        """Get a syntax theme instance."""
-        if isinstance(name, SyntaxTheme):
-            return name
-        theme: SyntaxTheme
-        if name in RICH_SYNTAX_THEMES:
-            theme = ANSISyntaxTheme(RICH_SYNTAX_THEMES[name])
-        else:
-            theme = PygmentsSyntaxTheme(name)
-        return theme
-
-    def __init__(
-        self,
-        code: str,
-        lexer: Union[Lexer, str],
-        *,
-        theme: Union[str, SyntaxTheme] = DEFAULT_THEME,
-        dedent: bool = False,
-        line_numbers: bool = False,
-        start_line: int = 1,
-        line_range: Optional[Tuple[Optional[int], Optional[int]]] = None,
-        highlight_lines: Optional[Set[int]] = None,
-        code_width: Optional[int] = None,
-        tab_size: int = 4,
-        word_wrap: bool = False,
-        background_color: Optional[str] = None,
-        indent_guides: bool = False,
-        padding: PaddingDimensions = 0,
-    ) -> None:
-        self.code = code
-        self._lexer = lexer
-        self.dedent = dedent
-        self.line_numbers = line_numbers
-        self.start_line = start_line
-        self.line_range = line_range
-        self.highlight_lines = highlight_lines or set()
-        self.code_width = code_width
-        self.tab_size = tab_size
-        self.word_wrap = word_wrap
-        self.background_color = background_color
-        self.background_style = (
-            Style(bgcolor=background_color) if background_color else Style()
-        )
-        self.indent_guides = indent_guides
-        self.padding = padding
-
-        self._theme = self.get_theme(theme)
-        self._stylized_ranges: List[_SyntaxHighlightRange] = []
-
-    @classmethod
-    def from_path(
-        cls,
-        path: str,
-        encoding: str = "utf-8",
-        lexer: Optional[Union[Lexer, str]] = None,
-        theme: Union[str, SyntaxTheme] = DEFAULT_THEME,
-        dedent: bool = False,
-        line_numbers: bool = False,
-        line_range: Optional[Tuple[int, int]] = None,
-        start_line: int = 1,
-        highlight_lines: Optional[Set[int]] = None,
-        code_width: Optional[int] = None,
-        tab_size: int = 4,
-        word_wrap: bool = False,
-        background_color: Optional[str] = None,
-        indent_guides: bool = False,
-        padding: PaddingDimensions = 0,
-    ) -> "Syntax":
-        """Construct a Syntax object from a file.
-
-        Args:
-            path (str): Path to file to highlight.
-            encoding (str): Encoding of file.
-            lexer (str | Lexer, optional): Lexer to use. If None, lexer will be auto-detected from path/file content.
-            theme (str, optional): Color theme, aka Pygments style (see https://pygments.org/docs/styles/#getting-a-list-of-available-styles). Defaults to "emacs".
-            dedent (bool, optional): Enable stripping of initial whitespace. Defaults to True.
-            line_numbers (bool, optional): Enable rendering of line numbers. Defaults to False.
-            start_line (int, optional): Starting number for line numbers. Defaults to 1.
-            line_range (Tuple[int, int], optional): If given should be a tuple of the start and end line to render.
-            highlight_lines (Set[int]): A set of line numbers to highlight.
-            code_width: Width of code to render (not including line numbers), or ``None`` to use all available width.
-            tab_size (int, optional): Size of tabs. Defaults to 4.
-            word_wrap (bool, optional): Enable word wrapping of code.
-            background_color (str, optional): Optional background color, or None to use theme color. Defaults to None.
-            indent_guides (bool, optional): Show indent guides. Defaults to False.
-            padding (PaddingDimensions): Padding to apply around the syntax. Defaults to 0 (no padding).
-
-        Returns:
-            [Syntax]: A Syntax object that may be printed to the console
-        """
-        code = Path(path).read_text(encoding=encoding)
-
-        if not lexer:
-            lexer = cls.guess_lexer(path, code=code)
-
-        return cls(
-            code,
-            lexer,
-            theme=theme,
-            dedent=dedent,
-            line_numbers=line_numbers,
-            line_range=line_range,
-            start_line=start_line,
-            highlight_lines=highlight_lines,
-            code_width=code_width,
-            tab_size=tab_size,
-            word_wrap=word_wrap,
-            background_color=background_color,
-            indent_guides=indent_guides,
-            padding=padding,
-        )
-
-    @classmethod
-    def guess_lexer(cls, path: str, code: Optional[str] = None) -> str:
-        """Guess the alias of the Pygments lexer to use based on a path and an optional string of code.
-        If code is supplied, it will use a combination of the code and the filename to determine the
-        best lexer to use. For example, if the file is ``index.html`` and the file contains Django
-        templating syntax, then "html+django" will be returned. If the file is ``index.html``, and no
-        templating language is used, the "html" lexer will be used. If no string of code
-        is supplied, the lexer will be chosen based on the file extension..
-
-        Args:
-             path (AnyStr): The path to the file containing the code you wish to know the lexer for.
-             code (str, optional): Optional string of code that will be used as a fallback if no lexer
-                is found for the supplied path.
-
-        Returns:
-            str: The name of the Pygments lexer that best matches the supplied path/code.
-        """
-        lexer: Optional[Lexer] = None
-        lexer_name = "default"
-        if code:
-            try:
-                lexer = guess_lexer_for_filename(path, code)
-            except ClassNotFound:
-                pass
-
-        if not lexer:
-            try:
-                _, ext = os.path.splitext(path)
-                if ext:
-                    extension = ext.lstrip(".").lower()
-                    lexer = get_lexer_by_name(extension)
-            except ClassNotFound:
-                pass
-
-        if lexer:
-            if lexer.aliases:
-                lexer_name = lexer.aliases[0]
-            else:
-                lexer_name = lexer.name
-
-        return lexer_name
-
-    def _get_base_style(self) -> Style:
-        """Get the base style."""
-        default_style = self._theme.get_background_style() + self.background_style
-        return default_style
-
-    def _get_token_color(self, token_type: TokenType) -> Optional[Color]:
-        """Get a color (if any) for the given token.
-
-        Args:
-            token_type (TokenType): A token type tuple from Pygments.
-
-        Returns:
-            Optional[Color]: Color from theme, or None for no color.
-        """
-        style = self._theme.get_style_for_token(token_type)
-        return style.color
-
-    @property
-    def lexer(self) -> Optional[Lexer]:
-        """The lexer for this syntax, or None if no lexer was found.
-
-        Tries to find the lexer by name if a string was passed to the constructor.
-        """
-
-        if isinstance(self._lexer, Lexer):
-            return self._lexer
-        try:
-            return get_lexer_by_name(
-                self._lexer,
-                stripnl=False,
-                ensurenl=True,
-                tabsize=self.tab_size,
-            )
-        except ClassNotFound:
-            return None
-
-    def highlight(
-        self,
-        code: str,
-        line_range: Optional[Tuple[Optional[int], Optional[int]]] = None,
-    ) -> Text:
-        """Highlight code and return a Text instance.
-
-        Args:
-            code (str): Code to highlight.
-            line_range(Tuple[int, int], optional): Optional line range to highlight.
-
-        Returns:
-            Text: A text instance containing highlighted syntax.
-        """
-
-        base_style = self._get_base_style()
-        justify: JustifyMethod = (
-            "default" if base_style.transparent_background else "left"
-        )
-
-        text = Text(
-            justify=justify,
-            style=base_style,
-            tab_size=self.tab_size,
-            no_wrap=not self.word_wrap,
-        )
-        _get_theme_style = self._theme.get_style_for_token
-
-        lexer = self.lexer
-
-        if lexer is None:
-            text.append(code)
-        else:
-            if line_range:
-                # More complicated path to only stylize a portion of the code
-                # This speeds up further operations as there are less spans to process
-                line_start, line_end = line_range
-
-                def line_tokenize() -> Iterable[Tuple[Any, str]]:
-                    """Split tokens to one per line."""
-                    assert lexer  # required to make MyPy happy - we know lexer is not None at this point
-
-                    for token_type, token in lexer.get_tokens(code):
-                        while token:
-                            line_token, new_line, token = token.partition("\n")
-                            yield token_type, line_token + new_line
-
-                def tokens_to_spans() -> Iterable[Tuple[str, Optional[Style]]]:
-                    """Convert tokens to spans."""
-                    tokens = iter(line_tokenize())
-                    line_no = 0
-                    _line_start = line_start - 1 if line_start else 0
-
-                    # Skip over tokens until line start
-                    while line_no < _line_start:
-                        try:
-                            _token_type, token = next(tokens)
-                        except StopIteration:
-                            break
-                        yield (token, None)
-                        if token.endswith("\n"):
-                            line_no += 1
-                    # Generate spans until line end
-                    for token_type, token in tokens:
-                        yield (token, _get_theme_style(token_type))
-                        if token.endswith("\n"):
-                            line_no += 1
-                            if line_end and line_no >= line_end:
-                                break
-
-                text.append_tokens(tokens_to_spans())
-
-            else:
-                text.append_tokens(
-                    (token, _get_theme_style(token_type))
-                    for token_type, token in lexer.get_tokens(code)
-                )
-            if self.background_color is not None:
-                text.stylize(f"on {self.background_color}")
-
-        if self._stylized_ranges:
-            self._apply_stylized_ranges(text)
-
-        return text
-
-    def stylize_range(
-        self, style: StyleType, start: SyntaxPosition, end: SyntaxPosition
-    ) -> None:
-        """
-        Adds a custom style on a part of the code, that will be applied to the syntax display when it's rendered.
-        Line numbers are 1-based, while column indexes are 0-based.
-
-        Args:
-            style (StyleType): The style to apply.
-            start (Tuple[int, int]): The start of the range, in the form `[line number, column index]`.
-            end (Tuple[int, int]): The end of the range, in the form `[line number, column index]`.
-        """
-        self._stylized_ranges.append(_SyntaxHighlightRange(style, start, end))
-
-    def _get_line_numbers_color(self, blend: float = 0.3) -> Color:
-        background_style = self._theme.get_background_style() + self.background_style
-        background_color = background_style.bgcolor
-        if background_color is None or background_color.is_system_defined:
-            return Color.default()
-        foreground_color = self._get_token_color(Token.Text)
-        if foreground_color is None or foreground_color.is_system_defined:
-            return foreground_color or Color.default()
-        new_color = blend_rgb(
-            background_color.get_truecolor(),
-            foreground_color.get_truecolor(),
-            cross_fade=blend,
-        )
-        return Color.from_triplet(new_color)
-
-    @property
-    def _numbers_column_width(self) -> int:
-        """Get the number of characters used to render the numbers column."""
-        column_width = 0
-        if self.line_numbers:
-            column_width = (
-                len(str(self.start_line + self.code.count("\n")))
-                + NUMBERS_COLUMN_DEFAULT_PADDING
-            )
-        return column_width
-
-    def _get_number_styles(self, console: Console) -> Tuple[Style, Style, Style]:
-        """Get background, number, and highlight styles for line numbers."""
-        background_style = self._get_base_style()
-        if background_style.transparent_background:
-            return Style.null(), Style(dim=True), Style.null()
-        if console.color_system in ("256", "truecolor"):
-            number_style = Style.chain(
-                background_style,
-                self._theme.get_style_for_token(Token.Text),
-                Style(color=self._get_line_numbers_color()),
-                self.background_style,
-            )
-            highlight_number_style = Style.chain(
-                background_style,
-                self._theme.get_style_for_token(Token.Text),
-                Style(bold=True, color=self._get_line_numbers_color(0.9)),
-                self.background_style,
-            )
-        else:
-            number_style = background_style + Style(dim=True)
-            highlight_number_style = background_style + Style(dim=False)
-        return background_style, number_style, highlight_number_style
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-
-        _, right, _, left = Padding.unpack(self.padding)
-        padding = left + right
-        if self.code_width is not None:
-            width = self.code_width + self._numbers_column_width + padding + 1
-            return Measurement(self._numbers_column_width, width)
-        lines = self.code.splitlines()
-        width = (
-            self._numbers_column_width
-            + padding
-            + (max(cell_len(line) for line in lines) if lines else 0)
-        )
-        if self.line_numbers:
-            width += 1
-        return Measurement(self._numbers_column_width, width)
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        segments = Segments(self._get_syntax(console, options))
-        if self.padding:
-            yield Padding(
-                segments, style=self._theme.get_background_style(), pad=self.padding
-            )
-        else:
-            yield segments
-
-    def _get_syntax(
-        self,
-        console: Console,
-        options: ConsoleOptions,
-    ) -> Iterable[Segment]:
-        """
-        Get the Segments for the Syntax object, excluding any vertical/horizontal padding
-        """
-        transparent_background = self._get_base_style().transparent_background
-        code_width = (
-            (
-                (options.max_width - self._numbers_column_width - 1)
-                if self.line_numbers
-                else options.max_width
-            )
-            if self.code_width is None
-            else self.code_width
-        )
-
-        ends_on_nl, processed_code = self._process_code(self.code)
-        text = self.highlight(processed_code, self.line_range)
-
-        if not self.line_numbers and not self.word_wrap and not self.line_range:
-            if not ends_on_nl:
-                text.remove_suffix("\n")
-            # Simple case of just rendering text
-            style = (
-                self._get_base_style()
-                + self._theme.get_style_for_token(Comment)
-                + Style(dim=True)
-                + self.background_style
-            )
-            if self.indent_guides and not options.ascii_only:
-                text = text.with_indent_guides(self.tab_size, style=style)
-                text.overflow = "crop"
-            if style.transparent_background:
-                yield from console.render(
-                    text, options=options.update(width=code_width)
-                )
-            else:
-                syntax_lines = console.render_lines(
-                    text,
-                    options.update(width=code_width, height=None, justify="left"),
-                    style=self.background_style,
-                    pad=True,
-                    new_lines=True,
-                )
-                for syntax_line in syntax_lines:
-                    yield from syntax_line
-            return
-
-        start_line, end_line = self.line_range or (None, None)
-        line_offset = 0
-        if start_line:
-            line_offset = max(0, start_line - 1)
-        lines: Union[List[Text], Lines] = text.split("\n", allow_blank=ends_on_nl)
-        if self.line_range:
-            if line_offset > len(lines):
-                return
-            lines = lines[line_offset:end_line]
-
-        if self.indent_guides and not options.ascii_only:
-            style = (
-                self._get_base_style()
-                + self._theme.get_style_for_token(Comment)
-                + Style(dim=True)
-                + self.background_style
-            )
-            lines = (
-                Text("\n")
-                .join(lines)
-                .with_indent_guides(self.tab_size, style=style)
-                .split("\n", allow_blank=True)
-            )
-
-        numbers_column_width = self._numbers_column_width
-        render_options = options.update(width=code_width)
-
-        highlight_line = self.highlight_lines.__contains__
-        _Segment = Segment
-        new_line = _Segment("\n")
-
-        line_pointer = "> " if options.legacy_windows else "❱ "
-
-        (
-            background_style,
-            number_style,
-            highlight_number_style,
-        ) = self._get_number_styles(console)
-
-        for line_no, line in enumerate(lines, self.start_line + line_offset):
-            if self.word_wrap:
-                wrapped_lines = console.render_lines(
-                    line,
-                    render_options.update(height=None, justify="left"),
-                    style=background_style,
-                    pad=not transparent_background,
-                )
-            else:
-                segments = list(line.render(console, end=""))
-                if options.no_wrap:
-                    wrapped_lines = [segments]
-                else:
-                    wrapped_lines = [
-                        _Segment.adjust_line_length(
-                            segments,
-                            render_options.max_width,
-                            style=background_style,
-                            pad=not transparent_background,
-                        )
-                    ]
-
-            if self.line_numbers:
-                wrapped_line_left_pad = _Segment(
-                    " " * numbers_column_width + " ", background_style
-                )
-                for first, wrapped_line in loop_first(wrapped_lines):
-                    if first:
-                        line_column = str(line_no).rjust(numbers_column_width - 2) + " "
-                        if highlight_line(line_no):
-                            yield _Segment(line_pointer, Style(color="red"))
-                            yield _Segment(line_column, highlight_number_style)
-                        else:
-                            yield _Segment("  ", highlight_number_style)
-                            yield _Segment(line_column, number_style)
-                    else:
-                        yield wrapped_line_left_pad
-                    yield from wrapped_line
-                    yield new_line
-            else:
-                for wrapped_line in wrapped_lines:
-                    yield from wrapped_line
-                    yield new_line
-
-    def _apply_stylized_ranges(self, text: Text) -> None:
-        """
-        Apply stylized ranges to a text instance,
-        using the given code to determine the right portion to apply the style to.
-
-        Args:
-            text (Text): Text instance to apply the style to.
-        """
-        code = text.plain
-        newlines_offsets = [
-            # Let's add outer boundaries at each side of the list:
-            0,
-            # N.B. using "\n" here is much faster than using metacharacters such as "^" or "\Z":
-            *[
-                match.start() + 1
-                for match in re.finditer("\n", code, flags=re.MULTILINE)
-            ],
-            len(code) + 1,
-        ]
-
-        for stylized_range in self._stylized_ranges:
-            start = _get_code_index_for_syntax_position(
-                newlines_offsets, stylized_range.start
-            )
-            end = _get_code_index_for_syntax_position(
-                newlines_offsets, stylized_range.end
-            )
-            if start is not None and end is not None:
-                text.stylize(stylized_range.style, start, end)
-
-    def _process_code(self, code: str) -> Tuple[bool, str]:
-        """
-        Applies various processing to a raw code string
-        (normalises it so it always ends with a line return, dedents it if necessary, etc.)
-
-        Args:
-            code (str): The raw code string to process
-
-        Returns:
-            Tuple[bool, str]: the boolean indicates whether the raw code ends with a line return,
-                while the string is the processed code.
-        """
-        ends_on_nl = code.endswith("\n")
-        processed_code = code if ends_on_nl else code + "\n"
-        processed_code = (
-            textwrap.dedent(processed_code) if self.dedent else processed_code
-        )
-        processed_code = processed_code.expandtabs(self.tab_size)
-        return ends_on_nl, processed_code
-
-
-def _get_code_index_for_syntax_position(
-    newlines_offsets: Sequence[int], position: SyntaxPosition
-) -> Optional[int]:
-    """
-    Returns the index of the code string for the given positions.
-
-    Args:
-        newlines_offsets (Sequence[int]): The offset of each newline character found in the code snippet.
-        position (SyntaxPosition): The position to search for.
-
-    Returns:
-        Optional[int]: The index of the code string for this position, or `None`
-            if the given position's line number is out of range (if it's the column that is out of range
-            we silently clamp its value so that it reaches the end of the line)
-    """
-    lines_count = len(newlines_offsets)
-
-    line_number, column_index = position
-    if line_number > lines_count or len(newlines_offsets) < (line_number + 1):
-        return None  # `line_number` is out of range
-    line_index = line_number - 1
-    line_length = newlines_offsets[line_index + 1] - newlines_offsets[line_index] - 1
-    # If `column_index` is out of range: let's silently clamp it:
-    column_index = min(line_length, column_index)
-    return newlines_offsets[line_index] + column_index
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    import argparse
-    import sys
-
-    parser = argparse.ArgumentParser(
-        description="Render syntax to the console with Rich"
-    )
-    parser.add_argument(
-        "path",
-        metavar="PATH",
-        help="path to file, or - for stdin",
-    )
-    parser.add_argument(
-        "-c",
-        "--force-color",
-        dest="force_color",
-        action="store_true",
-        default=None,
-        help="force color for non-terminals",
-    )
-    parser.add_argument(
-        "-i",
-        "--indent-guides",
-        dest="indent_guides",
-        action="store_true",
-        default=False,
-        help="display indent guides",
-    )
-    parser.add_argument(
-        "-l",
-        "--line-numbers",
-        dest="line_numbers",
-        action="store_true",
-        help="render line numbers",
-    )
-    parser.add_argument(
-        "-w",
-        "--width",
-        type=int,
-        dest="width",
-        default=None,
-        help="width of output (default will auto-detect)",
-    )
-    parser.add_argument(
-        "-r",
-        "--wrap",
-        dest="word_wrap",
-        action="store_true",
-        default=False,
-        help="word wrap long lines",
-    )
-    parser.add_argument(
-        "-s",
-        "--soft-wrap",
-        action="store_true",
-        dest="soft_wrap",
-        default=False,
-        help="enable soft wrapping mode",
-    )
-    parser.add_argument(
-        "-t", "--theme", dest="theme", default="monokai", help="pygments theme"
-    )
-    parser.add_argument(
-        "-b",
-        "--background-color",
-        dest="background_color",
-        default=None,
-        help="Override background color",
-    )
-    parser.add_argument(
-        "-x",
-        "--lexer",
-        default=None,
-        dest="lexer_name",
-        help="Lexer name",
-    )
-    parser.add_argument(
-        "-p", "--padding", type=int, default=0, dest="padding", help="Padding"
-    )
-    parser.add_argument(
-        "--highlight-line",
-        type=int,
-        default=None,
-        dest="highlight_line",
-        help="The line number (not index!) to highlight",
-    )
-    args = parser.parse_args()
-
-    from pip._vendor.rich.console import Console
-
-    console = Console(force_terminal=args.force_color, width=args.width)
-
-    if args.path == "-":
-        code = sys.stdin.read()
-        syntax = Syntax(
-            code=code,
-            lexer=args.lexer_name,
-            line_numbers=args.line_numbers,
-            word_wrap=args.word_wrap,
-            theme=args.theme,
-            background_color=args.background_color,
-            indent_guides=args.indent_guides,
-            padding=args.padding,
-            highlight_lines={args.highlight_line},
-        )
-    else:
-        syntax = Syntax.from_path(
-            args.path,
-            lexer=args.lexer_name,
-            line_numbers=args.line_numbers,
-            word_wrap=args.word_wrap,
-            theme=args.theme,
-            background_color=args.background_color,
-            indent_guides=args.indent_guides,
-            padding=args.padding,
-            highlight_lines={args.highlight_line},
-        )
-    console.print(syntax, soft_wrap=args.soft_wrap)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/zipp.py

@@ -1,329 +0,0 @@
-import io
-import posixpath
-import zipfile
-import itertools
-import contextlib
-import sys
-import pathlib
-
-if sys.version_info < (3, 7):
-    from collections import OrderedDict
-else:
-    OrderedDict = dict
-
-
-__all__ = ['Path']
-
-
-def _parents(path):
-    """
-    Given a path with elements separated by
-    posixpath.sep, generate all parents of that path.
-
-    >>> list(_parents('b/d'))
-    ['b']
-    >>> list(_parents('/b/d/'))
-    ['/b']
-    >>> list(_parents('b/d/f/'))
-    ['b/d', 'b']
-    >>> list(_parents('b'))
-    []
-    >>> list(_parents(''))
-    []
-    """
-    return itertools.islice(_ancestry(path), 1, None)
-
-
-def _ancestry(path):
-    """
-    Given a path with elements separated by
-    posixpath.sep, generate all elements of that path
-
-    >>> list(_ancestry('b/d'))
-    ['b/d', 'b']
-    >>> list(_ancestry('/b/d/'))
-    ['/b/d', '/b']
-    >>> list(_ancestry('b/d/f/'))
-    ['b/d/f', 'b/d', 'b']
-    >>> list(_ancestry('b'))
-    ['b']
-    >>> list(_ancestry(''))
-    []
-    """
-    path = path.rstrip(posixpath.sep)
-    while path and path != posixpath.sep:
-        yield path
-        path, tail = posixpath.split(path)
-
-
-_dedupe = OrderedDict.fromkeys
-"""Deduplicate an iterable in original order"""
-
-
-def _difference(minuend, subtrahend):
-    """
-    Return items in minuend not in subtrahend, retaining order
-    with O(1) lookup.
-    """
-    return itertools.filterfalse(set(subtrahend).__contains__, minuend)
-
-
-class CompleteDirs(zipfile.ZipFile):
-    """
-    A ZipFile subclass that ensures that implied directories
-    are always included in the namelist.
-    """
-
-    @staticmethod
-    def _implied_dirs(names):
-        parents = itertools.chain.from_iterable(map(_parents, names))
-        as_dirs = (p + posixpath.sep for p in parents)
-        return _dedupe(_difference(as_dirs, names))
-
-    def namelist(self):
-        names = super(CompleteDirs, self).namelist()
-        return names + list(self._implied_dirs(names))
-
-    def _name_set(self):
-        return set(self.namelist())
-
-    def resolve_dir(self, name):
-        """
-        If the name represents a directory, return that name
-        as a directory (with the trailing slash).
-        """
-        names = self._name_set()
-        dirname = name + '/'
-        dir_match = name not in names and dirname in names
-        return dirname if dir_match else name
-
-    @classmethod
-    def make(cls, source):
-        """
-        Given a source (filename or zipfile), return an
-        appropriate CompleteDirs subclass.
-        """
-        if isinstance(source, CompleteDirs):
-            return source
-
-        if not isinstance(source, zipfile.ZipFile):
-            return cls(_pathlib_compat(source))
-
-        # Only allow for FastLookup when supplied zipfile is read-only
-        if 'r' not in source.mode:
-            cls = CompleteDirs
-
-        source.__class__ = cls
-        return source
-
-
-class FastLookup(CompleteDirs):
-    """
-    ZipFile subclass to ensure implicit
-    dirs exist and are resolved rapidly.
-    """
-
-    def namelist(self):
-        with contextlib.suppress(AttributeError):
-            return self.__names
-        self.__names = super(FastLookup, self).namelist()
-        return self.__names
-
-    def _name_set(self):
-        with contextlib.suppress(AttributeError):
-            return self.__lookup
-        self.__lookup = super(FastLookup, self)._name_set()
-        return self.__lookup
-
-
-def _pathlib_compat(path):
-    """
-    For path-like objects, convert to a filename for compatibility
-    on Python 3.6.1 and earlier.
-    """
-    try:
-        return path.__fspath__()
-    except AttributeError:
-        return str(path)
-
-
-class Path:
-    """
-    A pathlib-compatible interface for zip files.
-
-    Consider a zip file with this structure::
-
-        .
-        ├── a.txt
-        └── b
-            ├── c.txt
-            └── d
-                └── e.txt
-
-    >>> data = io.BytesIO()
-    >>> zf = zipfile.ZipFile(data, 'w')
-    >>> zf.writestr('a.txt', 'content of a')
-    >>> zf.writestr('b/c.txt', 'content of c')
-    >>> zf.writestr('b/d/e.txt', 'content of e')
-    >>> zf.filename = 'mem/abcde.zip'
-
-    Path accepts the zipfile object itself or a filename
-
-    >>> root = Path(zf)
-
-    From there, several path operations are available.
-
-    Directory iteration (including the zip file itself):
-
-    >>> a, b = root.iterdir()
-    >>> a
-    Path('mem/abcde.zip', 'a.txt')
-    >>> b
-    Path('mem/abcde.zip', 'b/')
-
-    name property:
-
-    >>> b.name
-    'b'
-
-    join with divide operator:
-
-    >>> c = b / 'c.txt'
-    >>> c
-    Path('mem/abcde.zip', 'b/c.txt')
-    >>> c.name
-    'c.txt'
-
-    Read text:
-
-    >>> c.read_text()
-    'content of c'
-
-    existence:
-
-    >>> c.exists()
-    True
-    >>> (b / 'missing.txt').exists()
-    False
-
-    Coercion to string:
-
-    >>> import os
-    >>> str(c).replace(os.sep, posixpath.sep)
-    'mem/abcde.zip/b/c.txt'
-
-    At the root, ``name``, ``filename``, and ``parent``
-    resolve to the zipfile. Note these attributes are not
-    valid and will raise a ``ValueError`` if the zipfile
-    has no filename.
-
-    >>> root.name
-    'abcde.zip'
-    >>> str(root.filename).replace(os.sep, posixpath.sep)
-    'mem/abcde.zip'
-    >>> str(root.parent)
-    'mem'
-    """
-
-    __repr = "{self.__class__.__name__}({self.root.filename!r}, {self.at!r})"
-
-    def __init__(self, root, at=""):
-        """
-        Construct a Path from a ZipFile or filename.
-
-        Note: When the source is an existing ZipFile object,
-        its type (__class__) will be mutated to a
-        specialized type. If the caller wishes to retain the
-        original type, the caller should either create a
-        separate ZipFile object or pass a filename.
-        """
-        self.root = FastLookup.make(root)
-        self.at = at
-
-    def open(self, mode='r', *args, pwd=None, **kwargs):
-        """
-        Open this entry as text or binary following the semantics
-        of ``pathlib.Path.open()`` by passing arguments through
-        to io.TextIOWrapper().
-        """
-        if self.is_dir():
-            raise IsADirectoryError(self)
-        zip_mode = mode[0]
-        if not self.exists() and zip_mode == 'r':
-            raise FileNotFoundError(self)
-        stream = self.root.open(self.at, zip_mode, pwd=pwd)
-        if 'b' in mode:
-            if args or kwargs:
-                raise ValueError("encoding args invalid for binary operation")
-            return stream
-        return io.TextIOWrapper(stream, *args, **kwargs)
-
-    @property
-    def name(self):
-        return pathlib.Path(self.at).name or self.filename.name
-
-    @property
-    def suffix(self):
-        return pathlib.Path(self.at).suffix or self.filename.suffix
-
-    @property
-    def suffixes(self):
-        return pathlib.Path(self.at).suffixes or self.filename.suffixes
-
-    @property
-    def stem(self):
-        return pathlib.Path(self.at).stem or self.filename.stem
-
-    @property
-    def filename(self):
-        return pathlib.Path(self.root.filename).joinpath(self.at)
-
-    def read_text(self, *args, **kwargs):
-        with self.open('r', *args, **kwargs) as strm:
-            return strm.read()
-
-    def read_bytes(self):
-        with self.open('rb') as strm:
-            return strm.read()
-
-    def _is_child(self, path):
-        return posixpath.dirname(path.at.rstrip("/")) == self.at.rstrip("/")
-
-    def _next(self, at):
-        return self.__class__(self.root, at)
-
-    def is_dir(self):
-        return not self.at or self.at.endswith("/")
-
-    def is_file(self):
-        return self.exists() and not self.is_dir()
-
-    def exists(self):
-        return self.at in self.root._name_set()
-
-    def iterdir(self):
-        if not self.is_dir():
-            raise ValueError("Can't listdir a file")
-        subs = map(self._next, self.root.namelist())
-        return filter(self._is_child, subs)
-
-    def __str__(self):
-        return posixpath.join(self.root.filename, self.at)
-
-    def __repr__(self):
-        return self.__repr.format(self=self)
-
-    def joinpath(self, *other):
-        next = posixpath.join(self.at, *map(_pathlib_compat, other))
-        return self._next(self.root.resolve_dir(next))
-
-    __truediv__ = joinpath
-
-    @property
-    def parent(self):
-        if not self.at:
-            return self.filename.parent
-        parent_at = posixpath.dirname(self.at.rstrip('/'))
-        if parent_at:
-            parent_at += '/'
-        return self._next(parent_at)

spaces/Avin1221/darkstorm2150-Protogen_x3.4_Official_Release/README.md

@@ -1,12 +0,0 @@
----
-title: Darkstorm2150-Protogen X3.4 Official Release
-emoji: 👀
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/symbols.py

@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
-       'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
-       'ong',
-       'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
-       'w', 'x', 'y', 'z', 'zh',
-        "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
-              'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
-              'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
-              'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
-    'ZH': 0,
-    "JA": 1,
-    "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    'ZH': 0,
-    "JA": num_zh_tones,
-    "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
-    a = set(zh_symbols)
-    b = set(en_symbols)
-    print(sorted(a&b))
-

spaces/Bart92/RVC_HF/demucs/model.py

@@ -1,202 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import julius
-from torch import nn
-
-from .utils import capture_init, center_trim
-
-
-class BLSTM(nn.Module):
-    def __init__(self, dim, layers=1):
-        super().__init__()
-        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
-        self.linear = nn.Linear(2 * dim, dim)
-
-    def forward(self, x):
-        x = x.permute(2, 0, 1)
-        x = self.lstm(x)[0]
-        x = self.linear(x)
-        x = x.permute(1, 2, 0)
-        return x
-
-
-def rescale_conv(conv, reference):
-    std = conv.weight.std().detach()
-    scale = (std / reference)**0.5
-    conv.weight.data /= scale
-    if conv.bias is not None:
-        conv.bias.data /= scale
-
-
-def rescale_module(module, reference):
-    for sub in module.modules():
-        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):
-            rescale_conv(sub, reference)
-
-
-class Demucs(nn.Module):
-    @capture_init
-    def __init__(self,
-                 sources,
-                 audio_channels=2,
-                 channels=64,
-                 depth=6,
-                 rewrite=True,
-                 glu=True,
-                 rescale=0.1,
-                 resample=True,
-                 kernel_size=8,
-                 stride=4,
-                 growth=2.,
-                 lstm_layers=2,
-                 context=3,
-                 normalize=False,
-                 samplerate=44100,
-                 segment_length=4 * 10 * 44100):
-        """
-        Args:
-            sources (list[str]): list of source names
-            audio_channels (int): stereo or mono
-            channels (int): first convolution channels
-            depth (int): number of encoder/decoder layers
-            rewrite (bool): add 1x1 convolution to each encoder layer
-                and a convolution to each decoder layer.
-                For the decoder layer, `context` gives the kernel size.
-            glu (bool): use glu instead of ReLU
-            resample_input (bool): upsample x2 the input and downsample /2 the output.
-            rescale (int): rescale initial weights of convolutions
-                to get their standard deviation closer to `rescale`
-            kernel_size (int): kernel size for convolutions
-            stride (int): stride for convolutions
-            growth (float): multiply (resp divide) number of channels by that
-                for each layer of the encoder (resp decoder)
-            lstm_layers (int): number of lstm layers, 0 = no lstm
-            context (int): kernel size of the convolution in the
-                decoder before the transposed convolution. If > 1,
-                will provide some context from neighboring time
-                steps.
-            samplerate (int): stored as meta information for easing
-                future evaluations of the model.
-            segment_length (int): stored as meta information for easing
-                future evaluations of the model. Length of the segments on which
-                the model was trained.
-        """
-
-        super().__init__()
-        self.audio_channels = audio_channels
-        self.sources = sources
-        self.kernel_size = kernel_size
-        self.context = context
-        self.stride = stride
-        self.depth = depth
-        self.resample = resample
-        self.channels = channels
-        self.normalize = normalize
-        self.samplerate = samplerate
-        self.segment_length = segment_length
-
-        self.encoder = nn.ModuleList()
-        self.decoder = nn.ModuleList()
-
-        if glu:
-            activation = nn.GLU(dim=1)
-            ch_scale = 2
-        else:
-            activation = nn.ReLU()
-            ch_scale = 1
-        in_channels = audio_channels
-        for index in range(depth):
-            encode = []
-            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]
-            if rewrite:
-                encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]
-            self.encoder.append(nn.Sequential(*encode))
-
-            decode = []
-            if index > 0:
-                out_channels = in_channels
-            else:
-                out_channels = len(self.sources) * audio_channels
-            if rewrite:
-                decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]
-            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]
-            if index > 0:
-                decode.append(nn.ReLU())
-            self.decoder.insert(0, nn.Sequential(*decode))
-            in_channels = channels
-            channels = int(growth * channels)
-
-        channels = in_channels
-
-        if lstm_layers:
-            self.lstm = BLSTM(channels, lstm_layers)
-        else:
-            self.lstm = None
-
-        if rescale:
-            rescale_module(self, reference=rescale)
-
-    def valid_length(self, length):
-        """
-        Return the nearest valid length to use with the model so that
-        there is no time steps left over in a convolutions, e.g. for all
-        layers, size of the input - kernel_size % stride = 0.
-
-        If the mixture has a valid length, the estimated sources
-        will have exactly the same length when context = 1. If context > 1,
-        the two signals can be center trimmed to match.
-
-        For training, extracts should have a valid length.For evaluation
-        on full tracks we recommend passing `pad = True` to :method:`forward`.
-        """
-        if self.resample:
-            length *= 2
-        for _ in range(self.depth):
-            length = math.ceil((length - self.kernel_size) / self.stride) + 1
-            length = max(1, length)
-            length += self.context - 1
-        for _ in range(self.depth):
-            length = (length - 1) * self.stride + self.kernel_size
-
-        if self.resample:
-            length = math.ceil(length / 2)
-        return int(length)
-
-    def forward(self, mix):
-        x = mix
-
-        if self.normalize:
-            mono = mix.mean(dim=1, keepdim=True)
-            mean = mono.mean(dim=-1, keepdim=True)
-            std = mono.std(dim=-1, keepdim=True)
-        else:
-            mean = 0
-            std = 1
-
-        x = (x - mean) / (1e-5 + std)
-
-        if self.resample:
-            x = julius.resample_frac(x, 1, 2)
-
-        saved = []
-        for encode in self.encoder:
-            x = encode(x)
-            saved.append(x)
-        if self.lstm:
-            x = self.lstm(x)
-        for decode in self.decoder:
-            skip = center_trim(saved.pop(-1), x)
-            x = x + skip
-            x = decode(x)
-
-        if self.resample:
-            x = julius.resample_frac(x, 2, 1)
-        x = x * std + mean
-        x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))
-        return x

spaces/Benson/text-generation/Examples/Arco Iris Seis Mvil Apk Beta.md

@@ -1,64 +0,0 @@
-<br />
-<h1>Rainbow Six Mobile: Todo lo que necesitas saber sobre la versión beta</h1>
-<p>¿Eres un fan de los juegos de disparos tácticos? ¿Quieres experimentar la emoción de Rainbow Six en tu teléfono? Si es así, entonces estás de suerte. Ubisoft ha lanzado recientemente la versión beta de Rainbow Six Mobile, un juego de disparos en primera persona multijugador competitivo y gratuito diseñado exclusivamente para dispositivos móviles. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo cómo descargarlo y jugarlo, por qué deberías jugarlo, y algunos consejos y trucos para dominarlo. ¡Vamos a empezar! </p>
-<h2>arco iris seis móvil apk beta</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://bltlly.com/2v6K6T">https://bltlly.com/2v6K6T</a></b></p><br /><br />
- <h2>¿Qué es Rainbow Six Mobile? </h2>
-<p>Rainbow Six Mobile es una adaptación móvil de la aclamada franquicia Rainbow Six, que es conocida por su jugabilidad realista y estratégica, su lista épica de operadores, sus mapas icónicos y sus modos de juego de área y bomba seguros. En Rainbow Six Mobile, puedes competir en partidas 5v5 de ritmo rápido como atacante o defensor, enfrentarte a intensos combates a corta distancia mientras tomas decisiones tácticas oportunas, colaborar como equipo para establecer estrategias y aprovechar al máximo los entornos destructibles, y elija entre una amplia selección de operadores altamente capacitados, cada uno con sus propias habilidades y dispositivos únicos. </p>
- <h3>Una breve introducción al juego y sus características</h3>
-
- <h3>Cómo descargar y jugar la versión beta en Android</h3>
-<p>La versión beta de Rainbow Six Mobile está disponible actualmente solo para dispositivos Android. Para descargarlo y reproducirlo, debes seguir estos pasos:</p>
-<ol>
-<li>Abre Google Play en tu teléfono Android. </li>
-<li>Buscar Rainbow Six Mobile o simplemente haga clic en este enlace. </li>
-<li> Seleccione el botón de registro previo, y pronto recibirá una notificación cuando el juego esté listo para descargar. </li>
-<li>Descarga e instala el juego en tu dispositivo. </li>
-<li>Inicie el juego e inicie sesión con su cuenta de Ubisoft o cree una si no tiene una. </li>
-<li>Elija su región y preferencias de idioma. </li>
-<li>Completa las misiones de tutorial para aprender los fundamentos del juego. </li>
-<li>Disfruta jugando Rainbow Six Mobile! </li>
-</ol>
- <h3>¿Cuáles son los requisitos del sistema y los problemas de compatibilidad</h3>
-<p>Rainbow Six Mobile es un juego que requiere un dispositivo de alto rendimiento para funcionar sin problemas. Los requisitos mínimos del sistema son:</p>
-<p></p>
-<ul>
-<li>Android 8.0 o superior</li>
-<li>4 GB de RAM o más</li>
-<li>Al menos 2 GB de espacio de almacenamiento gratuito</li>
-<li>Una conexión a Internet estable</li>
-</ul>
-<p>El juego también soporta algunos controladores externos, como Xbox One S Controller o PS4 DualShock 4 Controller. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego o el controlador. Puede consultar la lista de dispositivos y controladores compatibles en el sitio web oficial o en la página de Google Play. Si encuentras algún problema o error mientras juegas el juego, puedes reportarlo a los desarrolladores a través del sistema de retroalimentación del juego o los canales oficiales de las redes sociales. </p>
- <h2>¿Por qué usted debe jugar Rainbow Six móvil</h2>
-<p>Rainbow Six Mobile es un juego que ofrece mucha diversión y emoción para los fanáticos de los juegos de disparos tácticos. Estas son algunas de las razones por las que deberías jugar:</p>
- <h3>Los beneficios de jugar un juego de disparos tácticos en el móvil</h3>
-
- <h3>Las características únicas y modos de juego de Rainbow Six Mobile</h3>
-<p>Rainbow Six Mobile es un juego que tiene muchas características únicas y modos de juego que lo hacen destacar de otros juegos de disparos móviles. Por ejemplo, el juego tiene un motor de física realista e inmersivo que te permite interactuar con el entorno de varias maneras, como romper paredes, puertas, ventanas o pisos, crear nuevas líneas de visión o puntos de entrada, o usar la cubierta y la ocultación para tu ventaja. El juego también tiene un sistema de armas realista y auténtico que requiere que administres tu munición, retroceso y tiempo de recarga, así como personalizar tu carga con diferentes accesorios y pieles. El juego también tiene diferentes modos de juego que se adaptan a diferentes preferencias y niveles de habilidad, como el modo casual, el modo clasificado, el modo de entrenamiento y el modo de eventos especiales. </p>
- <h3>La lista diversa y personalizable de operadores y gadgets</h3>
-<p>Rainbow Six Mobile es un juego que tiene una lista diversa y personalizable de operadores y gadgets que le permiten crear su propio estilo de juego y estrategia. El juego cuenta con más de 20 operadores de diferentes países y unidades, cada uno con sus propias habilidades únicas y gadgets. Por ejemplo, puedes elegir a Ash, un atacante que puede usar sus rondas de asalto para destruir paredes o barricadas a distancia, o a Rook, un defensor que puede proporcionar a sus compañeros placas de armadura que aumentan su supervivencia. También puede desbloquear nuevos operadores al ganar créditos o comprarlos con dinero real. También puede personalizar sus operadores con diferentes trajes, tocados, encantos y emotes. </p>
- <h2>Consejos y trucos para dominar Rainbow Six móvil</h2>
-<p>Rainbow Six Mobile es un juego que requiere habilidad, estrategia y trabajo en equipo para ganar. Estos son algunos consejos y trucos para ayudarte a dominarlo:</p>
- <h3>Cómo elegir el mejor operador para tu estilo de juego y composición de equipo</h3>
-
- <h3>Cómo utilizar el entorno y los gadgets para su ventaja</h3>
-<p>Utilizar el entorno y los gadgets a tu favor es otro factor clave para tu éxito en Rainbow Six Mobile. Siempre debe estar al tanto de su entorno y utilizarlos para su beneficio. Por ejemplo, puedes usar objetos destructibles como paredes, pisos o techos para crear nuevas líneas de visión o puntos de entrada, o para exponer o sorprender a tus enemigos. También puedes usar objetos de cubierta como mesas, sillas o gabinetes para protegerte del fuego enemigo o para ocultar tus aparatos. También debe hacer uso de sus aparatos y utilizarlos sabiamente. Por ejemplo, puedes usar drones o cámaras para explorar el área y localizar enemigos u objetivos, o puedes usar granadas o flashbangs para despejar habitaciones o cegar enemigos. También puedes usar trampas o escudos para ralentizar o impedir que los enemigos avancen o entren. </p>
- <h3>Cómo comunicarse y coordinar con sus compañeros de equipo</h3>
-<p>Comunicarse y coordinar con sus compañeros de equipo es esencial para su éxito en Rainbow Six Mobile. Siempre debes comunicarte con tus compañeros de equipo y compartir información, como ubicaciones enemigas, estado de salud, uso de dispositivos o planes de estrategia. También debe coordinarse con sus compañeros de equipo y trabajar en equipo, como la creación de fuegos cruzados, maniobras de flanco, distracciones o emboscadas. Puedes usar la función de chat de voz en el juego o los comandos de chat rápido para comunicarte y coordinarte con tus compañeros de equipo. También puedes usar el sistema de ping para marcar ubicaciones, enemigos u objetos para que tus compañeros los vean. </p>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
-<h4>Aquí están algunas de las preguntas y respuestas más frecuentes sobre Rainbow Six Mobile:</h4>
-<ul>
-<li><b>Q: ¿Rainbow Six Mobile es gratuito? </b></li>
-<li>A: Sí, Rainbow Six Mobile es gratuito. Puedes descargarlo y jugarlo sin pagar nada. Sin embargo, el juego tiene algunas compras opcionales en el juego, como créditos, skins u operadores, que puedes comprar con dinero real si lo deseas. </li>
-<li><b>Q: ¿Rainbow Six Mobile está disponible para dispositivos iOS? </b></li>
-<li>A: Todavía no. Rainbow Six Mobile está disponible actualmente solo para dispositivos Android. Sin embargo, Ubisoft ha anunciado que está trabajando en llevar el juego a dispositivos iOS pronto. </li>
-<li><b>Q: ¿Cómo puedo obtener más créditos en Rainbow Six Mobile? </b></li>
-<li>A: Puedes obtener más créditos en Rainbow Six Mobile completando misiones, subiendo de nivel, participando en eventos, viendo anuncios o comprándolos con dinero real. </li>
-<li><b>Q: ¿Cómo puedo desbloquear más operadores en Rainbow Six Mobile? </b></li>
-<li>A: Puedes desbloquear más operadores en Rainbow Six Mobile al ganar créditos o comprarlos con dinero real. </li>
-<li><b>Q: ¿Cómo puedo reportar un error o un tramposo en Rainbow Six Mobile? </b></li>
-<li>A: Puedes reportar un error o un tramposo en Rainbow Six Mobile utilizando el sistema de retroalimentación del juego o poniéndote en contacto con los canales oficiales de las redes sociales. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Auto Chess War Mod Apk Terbaru.md

@@ -1,90 +0,0 @@
-<br />
-<h1>Auto Chess War Mod Apk Terbaru: Un juego de estrategia con un giro</h1>
-<p>Si usted está buscando un nuevo y emocionante juego de estrategia para jugar en su dispositivo móvil, es posible que desee echa un vistazo a la guerra de ajedrez auto mod apk terbaru. Este es un juego que combina los elementos de ajedrez, torre de defensa, y la recogida de cartas de una manera única. Tendrás que recoger diferentes héroes de varias clases y razas, colocarlos en un tablero de 8x8, y dejarlos luchar automáticamente contra otros jugadores o oponentes de IA. También tendrás que mejorar a tus héroes, combinarlos para crear otros más fuertes, usar objetos y habilidades para aumentar su rendimiento y derrotar a todos los enemigos para ser el último en pie. </p>
-<h2>auto chess war mod apk terbaru</h2><br /><p><b><b>Download Zip</b> &rarr;&rarr;&rarr; <a href="https://bltlly.com/2v6MhG">https://bltlly.com/2v6MhG</a></b></p><br /><br />
-<p>La guerra de ajedrez automática se basa en el mod original de Dota Auto Chess que se convirtió en un gran éxito entre los jugadores de Dota 2. Sin embargo, tiene su propio conjunto de héroes y objetos que no están relacionados con la tradición Dota. También tiene un estilo más colorido y caricaturesco que lo hace atractivo para un público más amplio. Si usted es un fan de los juegos de estrategia y los luchadores de automóviles, que sin duda disfrutar de la guerra de ajedrez auto mod apk terbaru. </p>
- <h2>Cómo descargar e instalar Auto Chess War Mod Apk Terbaru</h2>
-<p>Descargar e instalar auto chess war mod apk terbaru es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Descargar el archivo apk mod de una fuente de confianza. Puede encontrar el enlace al final de este artículo. </li>
-<li>Habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones que no son de la tienda de aplicaciones oficial. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>Instalar el archivo apk mod y lanzar el juego. Verá un nuevo icono en la pantalla de inicio o cajón de aplicaciones. Toque en él y disfrutar del juego. </li>
-</ol>
-<p>Nota: Es posible que tenga que desinstalar la versión original del juego antes de instalar el terbaru apk mod. Además, asegúrese de hacer una copia de seguridad de sus datos antes de hacerlo, ya que puede perder su progreso y logros. </p>
-
-<p>Auto ajedrez guerra mod apk terbaru tiene muchas características que harán que su experiencia de juego más divertido y satisfactorio. Estos son algunos de ellos:</p>
-<ul>
-<li>Desbloqueado todos los héroes y objetos. Usted tendrá acceso a todos los héroes y objetos en el juego, independientemente de su nivel o rango. Puedes elegir entre más de 50 héroes y 40 objetos, cada uno con sus propias habilidades y efectos. </li>
-<li>Oro y gemas ilimitados. Tendrás recursos ilimitados para comprar, actualizar y combinar tus héroes y objetos. Nunca te quedarás sin oro y gemas, que son las principales monedas del juego. </li>
-<li>No se requieren anuncios ni root. No verá ningún anuncio molesto o pop-ups mientras juega el juego. Usted tampoco tendrá que raíz de su dispositivo para utilizar el terbaru apk mod, que es seguro y conveniente. </li>
-</ul>
- <h2>Cómo jugar Auto Chess War Mod Apk Terbaru</h2>
-<p>Jugar guerra de ajedrez auto mod apk terbaru es muy fácil e intuitivo. Aquí están los pasos básicos:</p>
-<ol>
-<li>Elige a tus héroes de diferentes clases y razas. Hay seis clases (guerrero, mago, asesino, cazador, druida y sacerdote) y seis razas (humano, elfo, orco, no muerto, bestia y demonio) en el juego. Cada clase y raza tiene sus propias ventajas y desventajas, así como sinergias con otras clases y razas. Por ejemplo, tener tres guerreros en tu tablero les dará armadura extra, mientras que tener tres elfos les dará evasión adicional. </li>
-<li>Colócalas en el tablero de 8x8 y deja que luchen automáticamente. Tendrás un tiempo limitado para colocar a tus héroes en el tablero antes de que comience cada ronda. Puedes arrastrarlos y soltarlos en cualquier lugar de tu lado del tablero, o intercambiarlos con otros héroes. Una vez que la ronda comienza, tus héroes lucharán automáticamente contra los héroes del enemigo o la IA se arrastra. El ganador de cada ronda está determinado por quién tiene más héroes sobrevivientes o más puntos de salud. </li>
-
-<li>Usa objetos y habilidades para mejorar el rendimiento de tus héroes. Puedes obtener objetos de derrotar a los monstruos de la IA u otros jugadores. Puedes equipar objetos a tus héroes arrastrándolos y dejándolos caer sobre sus retratos. Cada héroe puede tener hasta tres objetos a la vez. Los objetos pueden aumentar las estadísticas de tus héroes o darles efectos especiales. Por ejemplo, un objeto llamado Máscara de locura puede aumentar la velocidad de ataque de tu héroe pero silenciarlos para que no usen habilidades. También puedes utilizar las habilidades que están disponibles para cada clase o carrera tocando sus iconos en la parte inferior de la pantalla. Las habilidades pueden tener varios efectos como sanar, dañar, impresionar o pulir a tus héroes. </li>
-<li>Derrota a todos los enemigos y sé el último en pie. Te enfrentarás a diferentes enemigos o la IA se arrastra en cada ronda hasta que solo quede un jugador. Perderás puntos de salud si pierdes una ronda o si no tienes espacio en tu tablero para nuevos héroes. El juego termina cuando tienes cero puntos de vida o cuando eres el único jugador que queda. </li>
-</ol>
- <h2>Consejos y trucos para Auto Chess War Mod Apk Terbaru</h2>
-<p>Para ayudarle a ganar más juegos en la guerra de ajedrez auto mod apk terbaru, aquí hay algunos consejos y trucos que puede utilizar:</p>
-<p></p>
-<ul>
-<li>Administre su economía sabiamente y ahorre oro para intereses. Ganará oro al ganar o perder rondas, completar misiones o vender héroes. También ganarás intereses en función de cuánto oro tengas al final de cada ronda. Cuanto más oro tengas, más intereses ganarás, hasta un máximo de 5 por ronda. Por lo tanto, es recomendable guardar su oro y gastarlo solo cuando sea necesario. </li>
-
-<li>Aprende las fortalezas y debilidades de cada héroe y artículo. Usted debe familiarizarse con las habilidades y efectos de cada héroe y objeto en el juego. Esto te ayudará a elegir los mejores para tu estrategia y contrarrestar los movimientos de tus oponentes. Por ejemplo, debes saber que un héroe llamado Lina puede hacer daño masivo con su habilidad final, pero también es muy frágil y vulnerable a los asesinos. También debes saber que un objeto llamado Blade Mail puede reflejar el daño del atacante, pero también reduce la armadura de tu héroe. </li>
-<li>Planifique sus transiciones de mediados y finales del juego en función de las estrategias de sus oponentes. No debes seguir con la misma estrategia a lo largo del juego, ya que puede ser ineficaz o anticuado a medida que el juego avanza. Siempre debes explorar los tableros de tus oponentes y ver lo que están construyendo y cómo lo están haciendo. Basado en esa información, usted debe planificar sus transiciones para contrarrestar sus estrategias o explotar sus debilidades. Por ejemplo, si ves que la mayoría de tus oponentes van a por magos, debes hacer la transición a guerreros o sacerdotes para resistir su daño mágico. </li>
-<li>Espía a tus oponentes más fuertes y ajusta tu formación en consecuencia. Siempre debes vigilar quién está liderando el juego y quién es la mayor amenaza para ti. Puedes espiar sus tablas tocando sus retratos en la parte superior de la pantalla. Puedes ver a sus héroes, objetos, habilidades y formación. En base a esa información, debe ajustar su formación para que coincida con la de ellos. Por ejemplo, si ves que tienen una línea de frente fuerte de guerreros, debes colocar a tus asesinos detrás de ellos para apuntar a su contraluz blanda. </li>
-</ul>
- <h2>Revisión de Auto Chess War Mod Apk Terbaru</h2>
-
-<tabla>
-<tr>
-<th>Pros</th>
-<th>Contras</th>
-</tr>
-<tr>
-<td>- Diversión: El juego es muy agradable y satisfactorio para jugar, especialmente cuando se gana un partido cercano o sacar un gran combo. </td>
-<td>- Repetitivo: El juego puede ser aburrido y monótono después de un tiempo, ya que no hay mucha variedad o innovación en el juego o el contenido. </td>
-</tr>
-<tr>
-<td>- Adictivo: El juego es muy atractivo y adictivo, ya que siempre querrás jugar una ronda más o probar una estrategia diferente. </td>
-<td>- Basado en la suerte: El juego depende en gran medida de la suerte y la aleatoriedad, ya que no puede obtener los héroes o artículos que desea o necesita de la tienda o cofres. </td>
-</tr>
-<tr>
-<td>- Desafiante: El juego es muy desafiante y estratégico, ya que tendrás que pensar rápido e inteligente para vencer a tus oponentes o enemigos de IA. </td>
-<td>- Inestable: El juego puede ser inestable y con errores a veces, ya que puede bloquearse o congelarse durante el juego o la carga. </td>
-</tr>
-<tr>
-<td>- Estratégico: El juego es muy estratégico y diverso, ya que tendrás que elegir entre diferentes clases y razas, utilizar diferentes objetos y habilidades, y adaptarse a diferentes situaciones. </td>
-<td>- Desequilibrado: El juego puede ser desequilibrado e injusto a veces, ya que algunos héroes u objetos pueden ser demasiado fuertes o débiles en comparación con otros. </td>
-</tr>
-<tr>
-<td>- Diverso: El juego es muy diverso y colorido, ya que tiene más de 50 héroes y 40 elementos con diferentes habilidades y efectos, así como un estilo caricaturesco que lo hace atractivo para una amplia audiencia. </td>
-<td>- Pago a ganar: El juego puede ser de pago a ganar a veces, ya que algunos héroes u objetos pueden estar bloqueados detrás de un muro de pago o requerir dinero real para obtener. </td>
-</tr>
-<tr>
-<td>- Gratis: El juego es gratis para descargar y jugar, lo que hace que sea accesible para cualquier persona que quiera probarlo. </td>
-<td>- Ninguno</td>
-</tr>
-</tabla>
- <h2>Conclusión</h2>
-
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes acerca de la guerra de ajedrez auto mod apk terbaru:</p>
-<ul>
-<li>Q1: ¿Cuál es la diferencia entre la guerra de ajedrez auto y otros juegos de batalla auto? </li>
-<li>A1: La guerra de ajedrez automática se basa en el mod original de Dota Auto Chess, pero con sus propios héroes y objetos. También tiene un estilo más caricaturesco y un ritmo más rápido que otros juegos de combate automático. </li>
-<li>Q2: ¿Cómo puedo obtener más oro y gemas en la guerra de ajedrez auto? </li>
-<li>A2: Puedes obtener más oro y gemas jugando el juego regularmente, completando misiones y logros, viendo anuncios, o usando el terbaru apk mod. </li>
-<li>Q3: ¿Cómo puedo desbloquear todos los héroes y objetos en la guerra de ajedrez automática? </li>
-<li>A3: Usted puede desbloquear todos los héroes y artículos mediante la nivelación de su cuenta, abrir cofres, o el uso de la terbaru apk mod. </li>
-<li>Q4: ¿Cómo puedo actualizar la guerra de ajedrez auto mod apk terbaru? </li>
-<li>A4: Puede actualizar auto ajedrez guerra mod apk terbaru mediante la descarga de la última versión de la misma fuente que lo consiguió de. Asegúrese de hacer una copia de seguridad de sus datos antes de actualizar. </li>
-<li>Q5: Es la guerra de ajedrez auto mod apk terbaru seguro de usar? </li>
-<li>A5: Auto guerra de ajedrez mod apk terbaru es seguro de usar, siempre y cuando se descarga desde una fuente de confianza. Sin embargo, usted debe tener en cuenta que el uso de mod apk puede violar los términos de servicio del juego y resultar en una prohibición u otras consecuencias. </li>
-</ul>
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spaces/Benson/text-generation/Examples/Brotato Zip Download.md

@@ -1,68 +0,0 @@
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-<h1>Descargar Brotato Zip: Cómo instalar y usar mods para tus juegos favoritos</h1>
- <p>¿Te encanta jugar juegos en tu PC, pero te gustaría poder personalizarlos a tu gusto? ¿Quieres probar nuevas características, gráficos, personajes o escenarios que no están disponibles en el juego original? Si es así, puede que te interese usar mods. </p>
- <p>Los mods son modificaciones o adiciones a un juego que son creadas por fans o desarrolladores. Pueden cambiar cualquier cosa, desde el juego, a las imágenes, al sonido, a la historia. Los mods pueden hacer un juego más divertido, desafiante, inmersivo o realista. </p>
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- <p>Sin embargo, instalar y usar mods puede ser complicado a veces. Necesitas encontrar mods compatibles, descargarlos, instalarlos y activarlos. También tienes que asegurarte de que no entren en conflicto entre ellos o con el juego en sí. Y necesitas mantenerlos actualizados y organizados. </p>
- <p>Ahí es donde entra Brotato Zip. Brotato Zip es un cargador mod que facilita la instalación y el uso de mods para varios juegos. También te conecta con una comunidad de modders y jugadores que comparten sus creaciones y comentarios. En este artículo, te mostraremos cómo descargar e instalar Brotato Zip, cómo descargar e instalar mods con él y cómo usarlos en tus juegos. </p>
- <h2>¿Qué es Brotato Zip? </h2>
- <h3>Un cargador mod para varios juegos</h3>
- <p>Brotato Zip es un cargador mod que te permite cargar múltiples mods a la vez para diferentes juegos. Funciona inyectando un . Archivo pck en la carpeta del juego, que luego carga los mods desde una carpeta separada. De esta forma, no tendrás que modificar los archivos del juego ni preocuparte por romper nada. </p>
- <p>Brotato Zip soporta muchos juegos populares, como Minecraft, Stardew Valley, Terraria, Among Us y más. Puedes consultar la lista de juegos compatibles en su página de GitHub. También planean agregar más juegos en el futuro. </p>
- <h3>Una comunidad de modders y jugadores</h3>
-
- <p>Brotato Zip tiene como objetivo crear una comunidad amigable y solidaria de modders y jugadores a los que les encanta jugar y mejorarlos. Dan la bienvenida a cualquiera que quiera unirse a ellos y divertirse. </p>
- <h2>¿Por qué utilizar Brotato Zip? </h2>
- <h3>Para mejorar su experiencia de juego</h3>
- <p>La razón principal por la que deberías usar Brotato Zip es porque puede hacer que tu experiencia de juego sea más agradable. Con Brotato Zip, puedes acceder a miles de mods que pueden añadir nuevos contenidos, características, gráficos, sonidos o mecánicas a tus juegos. También puede mezclar y combinar diferentes mods para crear sus propias combinaciones únicas. </p>
-<p></p>
- <p>Por ejemplo, puedes usar Brotato Zip para añadir nuevos biomas, mobs, elementos o estructuras a Minecraft. También puedes usarlo para cambiar la apariencia de tu personaje, granja o pueblo en Stardew Valley. También puedes usarlo para añadir nuevos roles, mapas o modos a Among Us. También puedes usarlo para mejorar los gráficos, la física o el modo de juego de Terraria. Y estos son solo algunos ejemplos de lo que se puede hacer con Brotato Zip.</p>
- <h3>Para apoyar a los creadores de mod</h3>
- <p>Otra razón por la que deberías usar Brotato Zip es porque puede ayudarte a apoyar a los creadores de mods. Los creadores de mods son personas que dedican su tiempo y esfuerzo a crear mods de forma gratuita. Lo hacen porque les encantan los juegos y quieren compartir sus creaciones con otros. También escuchan comentarios y actualizan sus mods regularmente. </p>
- <p>Usando Brotato Zip, puedes mostrar tu aprecio y gratitud a los creadores del mod. Puedes descargar sus mods, calificarlos, revisarlos y donarlos si quieres. También puede seguirlos en las redes sociales o unirse a sus servidores Discord. También puede darles sugerencias o informar de errores. Al hacer estas cosas, puedes ayudarles a mejorar sus mods y motivarlos a crear más. </p>
- <h2>¿Cómo descargar e instalar Brotato Zip? </h2>
- <h3>Descargar el archivo PCK de GitHub</h3>
-
- <p>Para descargar el archivo PCK, vaya a la página de GitHub y haga clic en el botón verde "Código". Luego, haga clic en "Descargar ZIP". Guarde el archivo ZIP en su computadora y extráigalo. Verá una carpeta llamada "Brotato-Zip-main". Dentro de esta carpeta, encontrará el archivo PCK llamado "Brotato.zip". Este es el archivo que necesita. </p>
- <h3>Instalar el archivo PCK como cualquier otro mod</h3>
- <p>El segundo paso para usar Brotato Zip es instalar el archivo PCK como cualquier otro mod. Esto significa que necesita copiarlo y pegarlo en la carpeta del juego. La carpeta del juego es donde se almacenan los archivos del juego. Normalmente se encuentra en su carpeta Archivos de programa o Steam. </p>
- <p>Para instalar el archivo PCK, busque su carpeta de juego y ábrala. Luego, encuentre la carpeta llamada "mods" o "modloader". Si no lo ve, cree uno usted mismo. Luego, copie y pegue el archivo PCK en esta carpeta. ¡Eso es todo! Usted ha instalado correctamente Brotato Zip.</p>
- <h2>¿Cómo descargar e instalar mods con Brotato Zip? </h2>
- <h3>Crea una carpeta de mods en tu carpeta de juego</h3>
- <p>El tercer paso para usar Brotato Zip es crear una carpeta mods en tu carpeta de juego. Aquí es donde almacenarás todos los mods que quieras usar con Brotato Zip. Es importante mantener tus mods organizados y separados de tus archivos de juego. </p>
- <p>Para crear una carpeta de mods, vuelva a su carpeta de juego y ábrala. Luego, haga clic derecho en un espacio vacío y seleccione "Nuevo" y luego "Carpeta". Nombra a esta carpeta "mods" o cualquier otra cosa que prefieras. Aquí es donde pondrás todas tus ZIPs mod.</p>
- <h3>Descargar mod ZIPs desde el sitio web de Brotato u otras fuentes</h3>
- <p>El cuarto paso para usar Brotato Zip es descargar mod ZIPs desde el sitio web de Brotato u otras fuentes. ZIPs Mod son archivos que contienen los datos mod que necesita cargar en su juego. Por lo general son comprimidos y fáciles de descargar. </p>
-
- <p>También puede descargar ZPI mod de otras fuentes, como Nexus Mods, CurseForge, ModDB u otros sitios web que albergan mods para varios juegos. Solo asegúrate de que son compatibles con Brotato Zip y que son seguros y libres de virus. </p>
- <h3>Añadir a la carpeta mods</h3>
- <p>El quinto paso para usar Brotato Zip es agregar las ZIPs mod que descargó a la carpeta mods que creó en su carpeta de juegos. Así es como los instalas en tu juego. </p>
- <p>Para agregarlos a la carpeta mods, busque los ZIPs mod que descargó y cópielos. Luego, vuelva a su carpeta de juegos y ábrala. Luego, abra la carpeta de mods que creó anteriormente y pegue las ZIPs de mod en ella. ¡Eso es todo! Has instalado correctamente los mods en tu juego. </p>
- <h2>¿Cómo usar mods con Brotato Zip? </h2>
- <h3>Iniciar el juego con el cargador mod habilitado</h3>
- <p>El sexto paso para usar Brotato Zip es lanzar el juego con el cargador mod habilitado. Así es como activas los mods que instalaste en tu juego. </p>
- <p>Para iniciar el juego con el cargador mod habilitado, vaya a su carpeta de juego y ábrala. Luego, encuentre el archivo ejecutable que ejecuta el juego. Por lo general se llama algo como "game.exe" o "launcher.exe". Haga clic derecho en él y seleccione "Ejecutar como administrador". Esto iniciará el juego con Brotato Zip corriendo en segundo plano. </p>
- <h3>Seleccione los mods que desea utilizar desde el menú</h3>
- <p>El séptimo y último paso para utilizar Brotato Zip es seleccionar los mods que desea utilizar en el menú. Así es como personalizas tu juego con los mods que instalaste. </p>
- <p>Para seleccionar los mods que desea utilizar, pulse la tecla F10 en el teclado mientras está en el juego. Esto abrirá un menú que muestra todos los mods que tiene en su carpeta de mods. Puede desplazarse por ellos y verificarlos o desmarcarlos como desee. También puede ordenarlos por nombre, fecha, tamaño o calificación. También puede buscar mods específicos escribiendo su nombre o palabra clave. </p>
-
- <h2>Conclusión</h2>
- <p>Brotato Zip es un cargador mod que facilita la instalación y el uso de mods para varios juegos. También te conecta con una comunidad de modders y jugadores que comparten sus creaciones y comentarios. Para usar Brotato Zip, necesitas descargar e instalar el archivo PCK desde su página GitHub, crear una carpeta mods en tu carpeta de juegos, descargar ZIPs mod desde su sitio web u otras fuentes, agregarlos a la carpeta mods, iniciar el juego con el cargador mod habilitado, y seleccione los mods que desea usar en el menú. </p>
- <p>Al usar Brotato Zip, puedes mejorar tu experiencia de juego, apoyar a los creadores de mods y divertirte con tus juegos. También puedes descubrir nuevos mods, aprender nuevas habilidades y hacer nuevos amigos. Brotato Zip es una herramienta que puede ayudarte a disfrutar de los juegos más que nunca. </p>
- <p>Si quieres saber más sobre Brotato Zip, visita su página web, únete a su servidor Discord o síguelos en Twitter. También puede consultar su canal de YouTube para obtener tutoriales, comentarios y vitrinas de diferentes mods. </p>
- <p>Gracias por leer este artículo. Esperamos que le resulte útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejarlos a continuación. Y si le gustó este artículo, por favor compártalo con sus amigos y familiares. </p>
- <h3>Preguntas frecuentes</h3>
- <ul>
-<li><b>¿Brotato Zip es seguro y legal? </b></li>
-<p>Sí, Brotato Zip es seguro y legal. No contiene virus ni malware, y no modifica ni daña tus archivos de juego. Tampoco viola los términos de servicio o derechos de autor de los juegos o los creadores de mods. Sin embargo, siempre debes usar Brotato Zip bajo tu propio riesgo y discreción, y hacer copias de seguridad de tus archivos de juego antes de usarlo. </p>
- <li><b>Brotato Zip funciona con juegos multijugador? </b></li>
-
- <li><b>¿Cómo actualizo Brotato Zip y los mods? </b></li>
-<p>Para actualizar Brotato Zip, es necesario descargar la última versión del archivo PCK desde su página GitHub y reemplazar el antiguo en su carpeta de juego. Para actualizar los mods, necesita descargar la última versión de los ZPI mod desde su sitio web u otras fuentes y reemplazar los antiguos en su carpeta mods. También puede utilizar el menú Brotato Zip para buscar actualizaciones y descargarlas automáticamente. </p>
- <li><b>¿Cómo desinstalo Brotato Zip y los mods? </b></li>
-<p>Para desinstalar Brotato Zip, es necesario eliminar el archivo PCK de la carpeta del juego. Para desinstalar los mods, debe eliminar los ZIPs mod de su carpeta mods. También puedes usar el menú Brotato Zip para desactivar o eliminar los mods que ya no quieras usar. </p>
- <li><b>¿Dónde puedo obtener ayuda o soporte para Brotato Zip y los mods? </b></li>
-<p>Si necesita ayuda o soporte para Brotato Zip y los mods, puede visitar su sitio web, unirse a su servidor Discord o seguirlos en Twitter. También puede consultar su canal de YouTube para obtener tutoriales, revisiones y vitrinas de diferentes mods. También puede ponerse en contacto con ellos por correo electrónico en [email protected]. </p>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/parser.py

@@ -1,294 +0,0 @@
-"""Base option parser setup"""
-
-import logging
-import optparse
-import shutil
-import sys
-import textwrap
-from contextlib import suppress
-from typing import Any, Dict, Generator, List, Tuple
-
-from pip._internal.cli.status_codes import UNKNOWN_ERROR
-from pip._internal.configuration import Configuration, ConfigurationError
-from pip._internal.utils.misc import redact_auth_from_url, strtobool
-
-logger = logging.getLogger(__name__)
-
-
-class PrettyHelpFormatter(optparse.IndentedHelpFormatter):
-    """A prettier/less verbose help formatter for optparse."""
-
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        # help position must be aligned with __init__.parseopts.description
-        kwargs["max_help_position"] = 30
-        kwargs["indent_increment"] = 1
-        kwargs["width"] = shutil.get_terminal_size()[0] - 2
-        super().__init__(*args, **kwargs)
-
-    def format_option_strings(self, option: optparse.Option) -> str:
-        return self._format_option_strings(option)
-
-    def _format_option_strings(
-        self, option: optparse.Option, mvarfmt: str = " <{}>", optsep: str = ", "
-    ) -> str:
-        """
-        Return a comma-separated list of option strings and metavars.
-
-        :param option:  tuple of (short opt, long opt), e.g: ('-f', '--format')
-        :param mvarfmt: metavar format string
-        :param optsep:  separator
-        """
-        opts = []
-
-        if option._short_opts:
-            opts.append(option._short_opts[0])
-        if option._long_opts:
-            opts.append(option._long_opts[0])
-        if len(opts) > 1:
-            opts.insert(1, optsep)
-
-        if option.takes_value():
-            assert option.dest is not None
-            metavar = option.metavar or option.dest.lower()
-            opts.append(mvarfmt.format(metavar.lower()))
-
-        return "".join(opts)
-
-    def format_heading(self, heading: str) -> str:
-        if heading == "Options":
-            return ""
-        return heading + ":\n"
-
-    def format_usage(self, usage: str) -> str:
-        """
-        Ensure there is only one newline between usage and the first heading
-        if there is no description.
-        """
-        msg = "\nUsage: {}\n".format(self.indent_lines(textwrap.dedent(usage), "  "))
-        return msg
-
-    def format_description(self, description: str) -> str:
-        # leave full control over description to us
-        if description:
-            if hasattr(self.parser, "main"):
-                label = "Commands"
-            else:
-                label = "Description"
-            # some doc strings have initial newlines, some don't
-            description = description.lstrip("\n")
-            # some doc strings have final newlines and spaces, some don't
-            description = description.rstrip()
-            # dedent, then reindent
-            description = self.indent_lines(textwrap.dedent(description), "  ")
-            description = f"{label}:\n{description}\n"
-            return description
-        else:
-            return ""
-
-    def format_epilog(self, epilog: str) -> str:
-        # leave full control over epilog to us
-        if epilog:
-            return epilog
-        else:
-            return ""
-
-    def indent_lines(self, text: str, indent: str) -> str:
-        new_lines = [indent + line for line in text.split("\n")]
-        return "\n".join(new_lines)
-
-
-class UpdatingDefaultsHelpFormatter(PrettyHelpFormatter):
-    """Custom help formatter for use in ConfigOptionParser.
-
-    This is updates the defaults before expanding them, allowing
-    them to show up correctly in the help listing.
-
-    Also redact auth from url type options
-    """
-
-    def expand_default(self, option: optparse.Option) -> str:
-        default_values = None
-        if self.parser is not None:
-            assert isinstance(self.parser, ConfigOptionParser)
-            self.parser._update_defaults(self.parser.defaults)
-            assert option.dest is not None
-            default_values = self.parser.defaults.get(option.dest)
-        help_text = super().expand_default(option)
-
-        if default_values and option.metavar == "URL":
-            if isinstance(default_values, str):
-                default_values = [default_values]
-
-            # If its not a list, we should abort and just return the help text
-            if not isinstance(default_values, list):
-                default_values = []
-
-            for val in default_values:
-                help_text = help_text.replace(val, redact_auth_from_url(val))
-
-        return help_text
-
-
-class CustomOptionParser(optparse.OptionParser):
-    def insert_option_group(
-        self, idx: int, *args: Any, **kwargs: Any
-    ) -> optparse.OptionGroup:
-        """Insert an OptionGroup at a given position."""
-        group = self.add_option_group(*args, **kwargs)
-
-        self.option_groups.pop()
-        self.option_groups.insert(idx, group)
-
-        return group
-
-    @property
-    def option_list_all(self) -> List[optparse.Option]:
-        """Get a list of all options, including those in option groups."""
-        res = self.option_list[:]
-        for i in self.option_groups:
-            res.extend(i.option_list)
-
-        return res
-
-
-class ConfigOptionParser(CustomOptionParser):
-    """Custom option parser which updates its defaults by checking the
-    configuration files and environmental variables"""
-
-    def __init__(
-        self,
-        *args: Any,
-        name: str,
-        isolated: bool = False,
-        **kwargs: Any,
-    ) -> None:
-        self.name = name
-        self.config = Configuration(isolated)
-
-        assert self.name
-        super().__init__(*args, **kwargs)
-
-    def check_default(self, option: optparse.Option, key: str, val: Any) -> Any:
-        try:
-            return option.check_value(key, val)
-        except optparse.OptionValueError as exc:
-            print(f"An error occurred during configuration: {exc}")
-            sys.exit(3)
-
-    def _get_ordered_configuration_items(
-        self,
-    ) -> Generator[Tuple[str, Any], None, None]:
-        # Configuration gives keys in an unordered manner. Order them.
-        override_order = ["global", self.name, ":env:"]
-
-        # Pool the options into different groups
-        section_items: Dict[str, List[Tuple[str, Any]]] = {
-            name: [] for name in override_order
-        }
-        for section_key, val in self.config.items():
-            # ignore empty values
-            if not val:
-                logger.debug(
-                    "Ignoring configuration key '%s' as it's value is empty.",
-                    section_key,
-                )
-                continue
-
-            section, key = section_key.split(".", 1)
-            if section in override_order:
-                section_items[section].append((key, val))
-
-        # Yield each group in their override order
-        for section in override_order:
-            for key, val in section_items[section]:
-                yield key, val
-
-    def _update_defaults(self, defaults: Dict[str, Any]) -> Dict[str, Any]:
-        """Updates the given defaults with values from the config files and
-        the environ. Does a little special handling for certain types of
-        options (lists)."""
-
-        # Accumulate complex default state.
-        self.values = optparse.Values(self.defaults)
-        late_eval = set()
-        # Then set the options with those values
-        for key, val in self._get_ordered_configuration_items():
-            # '--' because configuration supports only long names
-            option = self.get_option("--" + key)
-
-            # Ignore options not present in this parser. E.g. non-globals put
-            # in [global] by users that want them to apply to all applicable
-            # commands.
-            if option is None:
-                continue
-
-            assert option.dest is not None
-
-            if option.action in ("store_true", "store_false"):
-                try:
-                    val = strtobool(val)
-                except ValueError:
-                    self.error(
-                        "{} is not a valid value for {} option, "  # noqa
-                        "please specify a boolean value like yes/no, "
-                        "true/false or 1/0 instead.".format(val, key)
-                    )
-            elif option.action == "count":
-                with suppress(ValueError):
-                    val = strtobool(val)
-                with suppress(ValueError):
-                    val = int(val)
-                if not isinstance(val, int) or val < 0:
-                    self.error(
-                        "{} is not a valid value for {} option, "  # noqa
-                        "please instead specify either a non-negative integer "
-                        "or a boolean value like yes/no or false/true "
-                        "which is equivalent to 1/0.".format(val, key)
-                    )
-            elif option.action == "append":
-                val = val.split()
-                val = [self.check_default(option, key, v) for v in val]
-            elif option.action == "callback":
-                assert option.callback is not None
-                late_eval.add(option.dest)
-                opt_str = option.get_opt_string()
-                val = option.convert_value(opt_str, val)
-                # From take_action
-                args = option.callback_args or ()
-                kwargs = option.callback_kwargs or {}
-                option.callback(option, opt_str, val, self, *args, **kwargs)
-            else:
-                val = self.check_default(option, key, val)
-
-            defaults[option.dest] = val
-
-        for key in late_eval:
-            defaults[key] = getattr(self.values, key)
-        self.values = None
-        return defaults
-
-    def get_default_values(self) -> optparse.Values:
-        """Overriding to make updating the defaults after instantiation of
-        the option parser possible, _update_defaults() does the dirty work."""
-        if not self.process_default_values:
-            # Old, pre-Optik 1.5 behaviour.
-            return optparse.Values(self.defaults)
-
-        # Load the configuration, or error out in case of an error
-        try:
-            self.config.load()
-        except ConfigurationError as err:
-            self.exit(UNKNOWN_ERROR, str(err))
-
-        defaults = self._update_defaults(self.defaults.copy())  # ours
-        for option in self._get_all_options():
-            assert option.dest is not None
-            default = defaults.get(option.dest)
-            if isinstance(default, str):
-                opt_str = option.get_opt_string()
-                defaults[option.dest] = option.check_value(opt_str, default)
-        return optparse.Values(defaults)
-
-    def error(self, msg: str) -> None:
-        self.print_usage(sys.stderr)
-        self.exit(UNKNOWN_ERROR, f"{msg}\n")

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/webencodings/tests.py

@@ -1,153 +0,0 @@
-# coding: utf-8
-"""
-
-    webencodings.tests
-    ~~~~~~~~~~~~~~~~~~
-
-    A basic test suite for Encoding.
-
-    :copyright: Copyright 2012 by Simon Sapin
-    :license: BSD, see LICENSE for details.
-
-"""
-
-from __future__ import unicode_literals
-
-from . import (lookup, LABELS, decode, encode, iter_decode, iter_encode,
-               IncrementalDecoder, IncrementalEncoder, UTF8)
-
-
-def assert_raises(exception, function, *args, **kwargs):
-    try:
-        function(*args, **kwargs)
-    except exception:
-        return
-    else:  # pragma: no cover
-        raise AssertionError('Did not raise %s.' % exception)
-
-
-def test_labels():
-    assert lookup('utf-8').name == 'utf-8'
-    assert lookup('Utf-8').name == 'utf-8'
-    assert lookup('UTF-8').name == 'utf-8'
-    assert lookup('utf8').name == 'utf-8'
-    assert lookup('utf8').name == 'utf-8'
-    assert lookup('utf8 ').name == 'utf-8'
-    assert lookup(' \r\nutf8\t').name == 'utf-8'
-    assert lookup('u8') is None  # Python label.
-    assert lookup('utf-8 ') is None  # Non-ASCII white space.
-
-    assert lookup('US-ASCII').name == 'windows-1252'
-    assert lookup('iso-8859-1').name == 'windows-1252'
-    assert lookup('latin1').name == 'windows-1252'
-    assert lookup('LATIN1').name == 'windows-1252'
-    assert lookup('latin-1') is None
-    assert lookup('LATİN1') is None  # ASCII-only case insensitivity.
-
-
-def test_all_labels():
-    for label in LABELS:
-        assert decode(b'', label) == ('', lookup(label))
-        assert encode('', label) == b''
-        for repeat in [0, 1, 12]:
-            output, _ = iter_decode([b''] * repeat, label)
-            assert list(output) == []
-            assert list(iter_encode([''] * repeat, label)) == []
-        decoder = IncrementalDecoder(label)
-        assert decoder.decode(b'') == ''
-        assert decoder.decode(b'', final=True) == ''
-        encoder = IncrementalEncoder(label)
-        assert encoder.encode('') == b''
-        assert encoder.encode('', final=True) == b''
-    # All encoding names are valid labels too:
-    for name in set(LABELS.values()):
-        assert lookup(name).name == name
-
-
-def test_invalid_label():
-    assert_raises(LookupError, decode, b'\xEF\xBB\xBF\xc3\xa9', 'invalid')
-    assert_raises(LookupError, encode, 'é', 'invalid')
-    assert_raises(LookupError, iter_decode, [], 'invalid')
-    assert_raises(LookupError, iter_encode, [], 'invalid')
-    assert_raises(LookupError, IncrementalDecoder, 'invalid')
-    assert_raises(LookupError, IncrementalEncoder, 'invalid')
-
-
-def test_decode():
-    assert decode(b'\x80', 'latin1') == ('€', lookup('latin1'))
-    assert decode(b'\x80', lookup('latin1')) == ('€', lookup('latin1'))
-    assert decode(b'\xc3\xa9', 'utf8') == ('é', lookup('utf8'))
-    assert decode(b'\xc3\xa9', UTF8) == ('é', lookup('utf8'))
-    assert decode(b'\xc3\xa9', 'ascii') == ('é', lookup('ascii'))
-    assert decode(b'\xEF\xBB\xBF\xc3\xa9', 'ascii') == ('é', lookup('utf8'))  # UTF-8 with BOM
-
-    assert decode(b'\xFE\xFF\x00\xe9', 'ascii') == ('é', lookup('utf-16be'))  # UTF-16-BE with BOM
-    assert decode(b'\xFF\xFE\xe9\x00', 'ascii') == ('é', lookup('utf-16le'))  # UTF-16-LE with BOM
-    assert decode(b'\xFE\xFF\xe9\x00', 'ascii') == ('\ue900', lookup('utf-16be'))
-    assert decode(b'\xFF\xFE\x00\xe9', 'ascii') == ('\ue900', lookup('utf-16le'))
-
-    assert decode(b'\x00\xe9', 'UTF-16BE') == ('é', lookup('utf-16be'))
-    assert decode(b'\xe9\x00', 'UTF-16LE') == ('é', lookup('utf-16le'))
-    assert decode(b'\xe9\x00', 'UTF-16') == ('é', lookup('utf-16le'))
-
-    assert decode(b'\xe9\x00', 'UTF-16BE') == ('\ue900', lookup('utf-16be'))
-    assert decode(b'\x00\xe9', 'UTF-16LE') == ('\ue900', lookup('utf-16le'))
-    assert decode(b'\x00\xe9', 'UTF-16') == ('\ue900', lookup('utf-16le'))
-
-
-def test_encode():
-    assert encode('é', 'latin1') == b'\xe9'
-    assert encode('é', 'utf8') == b'\xc3\xa9'
-    assert encode('é', 'utf8') == b'\xc3\xa9'
-    assert encode('é', 'utf-16') == b'\xe9\x00'
-    assert encode('é', 'utf-16le') == b'\xe9\x00'
-    assert encode('é', 'utf-16be') == b'\x00\xe9'
-
-
-def test_iter_decode():
-    def iter_decode_to_string(input, fallback_encoding):
-        output, _encoding = iter_decode(input, fallback_encoding)
-        return ''.join(output)
-    assert iter_decode_to_string([], 'latin1') == ''
-    assert iter_decode_to_string([b''], 'latin1') == ''
-    assert iter_decode_to_string([b'\xe9'], 'latin1') == 'é'
-    assert iter_decode_to_string([b'hello'], 'latin1') == 'hello'
-    assert iter_decode_to_string([b'he', b'llo'], 'latin1') == 'hello'
-    assert iter_decode_to_string([b'hell', b'o'], 'latin1') == 'hello'
-    assert iter_decode_to_string([b'\xc3\xa9'], 'latin1') == 'é'
-    assert iter_decode_to_string([b'\xEF\xBB\xBF\xc3\xa9'], 'latin1') == 'é'
-    assert iter_decode_to_string([
-        b'\xEF\xBB\xBF', b'\xc3', b'\xa9'], 'latin1') == 'é'
-    assert iter_decode_to_string([
-        b'\xEF\xBB\xBF', b'a', b'\xc3'], 'latin1') == 'a\uFFFD'
-    assert iter_decode_to_string([
-        b'', b'\xEF', b'', b'', b'\xBB\xBF\xc3', b'\xa9'], 'latin1') == 'é'
-    assert iter_decode_to_string([b'\xEF\xBB\xBF'], 'latin1') == ''
-    assert iter_decode_to_string([b'\xEF\xBB'], 'latin1') == 'ï»'
-    assert iter_decode_to_string([b'\xFE\xFF\x00\xe9'], 'latin1') == 'é'
-    assert iter_decode_to_string([b'\xFF\xFE\xe9\x00'], 'latin1') == 'é'
-    assert iter_decode_to_string([
-        b'', b'\xFF', b'', b'', b'\xFE\xe9', b'\x00'], 'latin1') == 'é'
-    assert iter_decode_to_string([
-        b'', b'h\xe9', b'llo'], 'x-user-defined') == 'h\uF7E9llo'
-
-
-def test_iter_encode():
-    assert b''.join(iter_encode([], 'latin1')) == b''
-    assert b''.join(iter_encode([''], 'latin1')) == b''
-    assert b''.join(iter_encode(['é'], 'latin1')) == b'\xe9'
-    assert b''.join(iter_encode(['', 'é', '', ''], 'latin1')) == b'\xe9'
-    assert b''.join(iter_encode(['', 'é', '', ''], 'utf-16')) == b'\xe9\x00'
-    assert b''.join(iter_encode(['', 'é', '', ''], 'utf-16le')) == b'\xe9\x00'
-    assert b''.join(iter_encode(['', 'é', '', ''], 'utf-16be')) == b'\x00\xe9'
-    assert b''.join(iter_encode([
-        '', 'h\uF7E9', '', 'llo'], 'x-user-defined')) == b'h\xe9llo'
-
-
-def test_x_user_defined():
-    encoded = b'2,\x0c\x0b\x1aO\xd9#\xcb\x0f\xc9\xbbt\xcf\xa8\xca'
-    decoded = '2,\x0c\x0b\x1aO\uf7d9#\uf7cb\x0f\uf7c9\uf7bbt\uf7cf\uf7a8\uf7ca'
-    encoded = b'aa'
-    decoded = 'aa'
-    assert decode(encoded, 'x-user-defined') == (decoded, lookup('x-user-defined'))
-    assert encode(decoded, 'x-user-defined') == encoded

spaces/BramVanroy/llama-2-13b-chat-dutch-space/model.py

@@ -1,78 +0,0 @@
-from threading import Thread
-from typing import Iterator
-
-import torch
-from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-
-USE_QUANTIZATION = True
-if USE_QUANTIZATION:
-    model_id = "TheBloke/Llama-2-13B-Chat-Dutch-GPTQ"
-else:
-    model_id = "BramVanroy/Llama-2-13b-chat-dutch"
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-config = AutoConfig.from_pretrained(model_id)
-config.pretraining_tp = 1
-
-model = AutoModelForCausalLM.from_pretrained(model_id, config=config, torch_dtype=torch.float16, device_map="auto")
-
-# Torch.compile not supported on Windows
-try:
-    model = torch.compile(model)
-except Exception:
-    pass
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-
-def get_prompt(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> str:
-    texts = [f"<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n"]
-    # The first user input is _not_ stripped
-    do_strip = False
-    for user_input, response in chat_history:
-        user_input = user_input.strip() if do_strip else user_input
-        do_strip = True
-        texts.append(f"{user_input} [/INST] {response.strip()} </s><s>[INST] ")
-    message = message.strip() if do_strip else message
-    texts.append(f"{message} [/INST]")
-    return "".join(texts)
-
-
-def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int:
-    prompt = get_prompt(message, chat_history, system_prompt)
-    input_ids = tokenizer([prompt], return_tensors="np", add_special_tokens=False)["input_ids"]
-    return input_ids.shape[-1]
-
-
-def run(
-    message: str,
-    chat_history: list[tuple[str, str]],
-    system_prompt: str,
-    max_new_tokens: int = 1024,
-    temperature: float = 0.8,
-    top_p: float = 0.95,
-    top_k: int = 50,
-    no_repeat_ngram_size: int = 3,
-) -> Iterator[str]:
-    prompt = get_prompt(message, chat_history, system_prompt)
-    inputs = tokenizer([prompt], return_tensors="pt", add_special_tokens=False).to(device)
-
-    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
-    generate_kwargs = dict(
-        inputs,
-        streamer=streamer,
-        max_new_tokens=max_new_tokens,
-        do_sample=True,
-        top_p=top_p,
-        top_k=top_k,
-        temperature=temperature,
-        no_repeat_ngram_size=no_repeat_ngram_size,
-        num_beams=1,
-    )
-    t = Thread(target=model.generate, kwargs=generate_kwargs)
-    t.start()
-
-    outputs = []
-    for text in streamer:
-        outputs.append(text)
-        yield "".join(outputs)

spaces/BraydenMoore/a-random-unsecured-camera/templates/index.html

@@ -1,483 +0,0 @@
-<!DOCTYPE html>
-<html>
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<head>
-  
-  <meta property="og:title" content="a random unsecured camera" />
-  <meta property="og:image" content="{{ url_for('static', filename='searching.png') }}" />
-  <meta property="og:url" content="https://unsecuredcamera.com" />
-
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css">
-  <link rel="preload" href="{{ url_for('static', filename='eye.gif') }}" as="image">
-  <link rel="icon" type="image/png" href="https://i.ibb.co/gvKPXJD/eye.gif">
-    <title>{{ page_title|safe }}</title>
-    <style>
-        body {
-            justify-content: center;
-            background-color: black;
-            display: flex;
-            align-items:center;
-            height: 100vh;
-            margin: auto;
-        }
-      /* Webkit browsers like Chrome, Safari */
-        ::-webkit-scrollbar {
-            width: 10px;
-            border-radius: 5px;
-        }
-
-        ::-webkit-scrollbar-thumb {
-            background:  black;
-            border-radius: 5px;
-        }
-
-        ::-webkit-scrollbar-thumb:hover {
-            background:  black;
-        }
-
-        /* Firefox */
-        * {
-            scrollbar-width: thin;
-            scrollbar-color: black yellow;
-        }
-        h3 {
-            margin-bottom: 0px;
-            margin-top: 0px !important;
-            border-bottom: 2px solid yellow; 
-            color:rgb(53, 53, 53); 
-            font-family: 'Helvetica'; font-weight: 50;
-        }
-        .pulse {
-            width: 40px;
-            height: 40px;
-            border-radius: 50%;
-            position: absolute;
-            background-color: yellow;
-            opacity: 0.5;
-            animation: pulse-animation 5s infinite;
-            margin-left: -20px;
-            margin-top: -20px; 
-        }
-        .dot {
-            border-style: solid;
-            border-width: 2px;
-            border-color: black;
-            width: 5px;
-            height: 5px;
-            border-radius: 50%;
-            position: absolute;
-            background-color: yellow;
-            margin-left: -4.5px;
-            margin-top: -4.5px;
-        }
-        @keyframes pulse-animation {
-            0% { transform: scale(0.1); opacity: 0.4; }
-            100% { transform: scale(3); opacity: 0; }
-        }
-        .flex-container {
-            display: flex;
-            justify-content: left;
-            align-items: top;
-        }
-        .outer-container {
-            display: inline-block;
-            justify-content: left;
-            max-width: 70vw;
-        }
-        #feed-div {
-            display: flex;
-            justify-content: right; 
-            width: 90%;
-            height: 90%;
-            position: relative;
-            margin-right: 20px;
-        }
-        h1 {
-          margin: 0px;
-          margin-top: 20px;
-        }
-        .feed {
-          transition: opacity 0.3s ease;
-          width: 100%;
-          height: 100%;
-          max-height: 80vh;
-          border-radius: 5px;
-        }
-        .map-div {
-            position: relative;
-            width: 280px;
-            height: 190px;
-            margin-top: 3%;
-            margin-bottom: 3%;
-            box-sizing: border-box;
-        }
-        .info {
-            cursor: default;
-            display: flex;
-            flex-direction: column;
-            align-items: flex-start;
-            width: 300px;
-            transition 0.3s ease;
-        }
-        #info {
-            transition 0.5s ease;
-        }
-        #info-text {
-            margin-top: 20px;
-            margin-bottom: 20px;
-        }
-        a:hover {
-            background-color: yellow;
-            color: black;
-            transition: 0.5s ease;
-        }
-        a {
-            background-color: transparent;
-            border-radius: 5px;
-            text-decoration: none;
-            color:rgb(53, 53, 53);
-            transition: 0.5s ease;
-        }
-        .hoverButton {
-              color: rgb(53,53,53);
-        }
-        .tag {
-            font-family: 'Helvetica'; 
-            font-weight: 50;
-            margin: auto;
-            color:rgb(53,53,53);
-        }
-
-        #share {
-          margin: auto;
-          margin-left: 10px;
-          color: yellow;
-          transition: 0.3s ease;
-        }
-        #share:hover {
-          opacity: 0.5;
-        }
-        #show-more {
-          cursor: pointer;
-        }
-        #additional-info {
-          display: block;
-          height: 0px;
-          overflow: hidden;
-          transition: height 0.3s ease;
-        }
-        #additional-info.expanded {
-          height: auto; 
-        }
-
-
-        @media only screen and (orientation: portrait) {
-            .body {
-              height: 85vh;
-            }  
-            h1 {
-              margin-top: 20px !important;
-            }
-            .flex-container {
-                flex-direction: column;
-                align-items: left;
-            }
-
-            .map-div {
-                width: 100%;
-                height: 100%;
-            }
-            .outer-container {
-                max-width: 85vw;
-            }
-            #feed-div {
-                justify-content: left;
-                width: 100%;
-                max-height: 45%;
-                margin-right: 0;
-            }
-            .feed {
-              height: 100%;
-              width: 100%;
-              max-height: 100%;
-            }
-        }
-    </style>
-</head>
-
-<body style="background-color: black;">
-    <div class="outer-container">
-            <div class="flex-container">
-
-                    <div id="feed-div">
-                      <img id="feed" class="feed" src="static/eye.gif" />
-                    </div>
-
-              
-                    <div class="info" id="info">
-                      <h1 id="country" style="color:rgb(53, 53, 53); margin-top: 10px; font-family: 'Helvetica'; font-weight: 50; margin-bottom: 10px;"> searching...</h1>        
-                      <a href="{{ ip_link }}" target="_blank"> <h3 style="border-bottom: 2px solid yellow;" id="location-name">{{ page_title|safe }}</h3></a>
-
-                      <div style="display: flex; margin-top: 22px; margin-bottom: 0%;">
-  
-                        <a href="?new=true" style="margin-right: 10px; display: inline-block;">
-                          <button class="hoverButton" style="border-radius: 5px; border: 2px solid yellow; background-color: transparent; padding: 10px;">
-                            another
-                          </button>
-                        </a>
-                        <!--<a  class="abortButton" href="?new=false&id={{ id }}" id="refreshSameFeedButton" style="display: inline-block;">-->
-                        <a  class="abortButton" href="?new=false&id={{ id }}" id="refreshSameFeedButton" style="display: inline-block;">
-                          <button class="hoverButton" style="border-radius: 5px; border: 2px solid rgb(53, 53, 53); background-color: transparent; padding: 10px;">
-                            refresh
-                          </button>
-                        </a>
-                        
-                        <i id="share" class="fa-solid fa-link" data-id="{{ id }}"></i>
-                        <p id="copied" class="tag" style="visibility: hidden;">copied</p>
-                      </div>
-
-                        <p id="info-text" style="color:rgb(53, 53, 53); font-family: 'Helvetica'; font-weight: 50;">
-
-                          time: <span id="time"></span><br>
-
-                          <span id="additional-info">
-                            owner: {{ owner }}<br>
-                            ip: {{ ip }}<br>  
-                            lat, lon: {{ loc }}
-                          </span>
-                          
-                          <span id="show-more">more <i style="margin-bottom:20px; color:rgb(53, 53, 53);" id="more-button" class="fa-solid fa-caret-down"></i></span>
-
-                          <span class="tag"><br>a brayden moore website<br></span>
-                          thanks for visiting<br>
-                          <a href="https://huggingface.co/spaces/BraydenMoore/a-random-unsecured-camera/tree/main" target="_blank">see the code</a>
-                        
-                        </p>
-                    
-
-                      <!--
-                        <div class="map-div">
-                            <img id="map" src="{{ url_for('static', filename='map_populated.png') }}" style="width: 100%; height: 100%;" />
-                            <div class="dot" style="left: {{ X }}%; top: {{ Y }}%;"></div>
-                            <div class="pulse" style="left: {{ X }}%; top: {{ Y }}%;"></div>
-                        </div>
-                      -->
-                    </div>
-
-                </div>
-    </div>
-
-
-    <script>
-      
-     // Handle feed load and refresh
-    let loadingGif = "{{ url_for('static', filename='eye.gif') }}";
-    let currentFeed;
-    let firstLoad = true;
-    document.addEventListener("DOMContentLoaded", function() {
-        const feed = document.getElementById("feed");
-        feed.style.width = "80px";
-        feed.style.height = "50px";
-        feed.src = loadingGif;
-        feed.style.opacity = "0.2";
-        const infoDiv = document.getElementById("info");
-        const infoText = document.getElementById("info-text");
-        infoText.style.opacity = "0";
-
-        const countryElement = document.getElementById("country");
-      
-        const locationName = document.getElementById('location-name');
-        locationName.textContent = "{{ page_title|safe }}";
-      
-        const newUrl = "{{ url|safe }}";
-
-        let contentTypeChecked = false;
-        let isJpeg = false;
-        function refreshImage() {
-            if (!isJpeg && !contentTypeChecked) {
-                let xhr = new XMLHttpRequest();
-                xhr.open('HEAD', newUrl, true);
-                xhr.onreadystatechange = function() {
-                    if (xhr.readyState === 4 && xhr.status === 200) {
-                        if (xhr.getResponseHeader('Content-Type') === 'image/jpeg') {
-                            img.src = newUrl + "?r=" + new Date().getTime();
-                            if (firstLoad){
-                                countryElement.textContent = "connecting..."; 
-                            }
-                            isJpeg = true;
-                            contentTypeChecked = true;
-                        }
-                    }
-                };
-                xhr.send();
-            } else if (isJpeg && contentTypeChecked) {
-                img.src = newUrl + "?r=" + new Date().getTime();
-            }
-        }
-        
-        const img = new Image();
-        img.onload = function() {
-            if (firstLoad) {
-              infoDiv.style.opacity = "0";
-            }
-            firstLoad = false;
-            old = newUrl;
-            setTimeout(() => {
-              feed.src = this.src;
-              locationName.textContent = "{{ name|safe }}";
-              feed.style.width = "100%";
-              feed.style.height = "70%";
-              feed.style.opacity = "1";
-              infoDiv.style.opacity = "1";
-            }, 100);
-            const infoText = document.getElementById("info-text");
-            infoText.style.opacity = "1";
-          
-            setTimeout(refreshImage, 500);
-            country.textContent = "{{ country }}"
-        };
-        
-        img.onerror = function(event) {
-            feed.style.width = "80px";
-            feed.style.height = "50px";
-            feed.src = loadingGif;
-            feed.style.opacity = "0.2";
-            infoText.style.opacity = "0";
-            locationName.textContent = "{{ page_title|safe }}";
-            console.log("Image loading failed:", event);
-            
-            const urlParams = new URLSearchParams(window.location.search);
-            if (urlParams.get('new') === 'false') {
-                countryElement.textContent = "couldn't connect.";
-            }
-            else {
-                window.location.href = "?new=true";
-            }
-        };
-        
-        img.src = newUrl;
-    });
-
-
-    // Time count
-    document.addEventListener("DOMContentLoaded", function() {
-      const timezone = "{{ timezone }}"; 
-      setInterval(() => {
-        const now = new Date();
-        const options = { 
-          timeZone: timezone, 
-          hour: '2-digit', 
-          minute: '2-digit', 
-          second: '2-digit',
-          hour12: true
-        };
-        const timeString = now.toLocaleTimeString('en-US', options);
-        document.getElementById("time").textContent = timeString;
-      }, 200);
-    });
-
-    // Copy link
-    document.addEventListener("DOMContentLoaded", function() {
-      const shareIcon = document.getElementById("share");
-      const copiedText = document.getElementById("copied");
-
-      shareIcon.addEventListener("click", function() {
-          const id = this.getAttribute("data-id");
-          const urlToCopy = `${window.location.origin}/?new=false&id=${id}`;
-          
-          navigator.clipboard.writeText(urlToCopy).then(() => {
-              console.log("URL copied to clipboard");
-              shareIcon.className = "fa-solid fa-check";
-              //copiedText.style.visibility = "visible";
-          }).catch(err => {
-              console.log("Could not copy text: ", err);
-          });
-      });
-  });
-
-  // Connect vs search
-  document.addEventListener("DOMContentLoaded", function() {
-      const urlParams = new URLSearchParams(window.location.search);
-      const countryElement = document.getElementById("country");
-
-      if (urlParams.get('new') === 'false') {
-          countryElement.textContent = "connecting...";
-      }
-  });
-
-
-  // Fullscreen
-    document.addEventListener("DOMContentLoaded", function() {
-    const feed = document.getElementById("feed");
-    let isFullscreen = false; 
-
-    function toggleFullScreen() {
-        if (!isFullscreen) {
-            if (this.requestFullscreen) {
-                this.requestFullscreen();
-            } else if (this.mozRequestFullScreen) {
-                this.mozRequestFullScreen();
-            } else if (this.webkitRequestFullscreen) {
-                this.webkitRequestFullscreen();
-            } else if (this.msRequestFullscreen) {
-                this.msRequestFullscreen();
-            }
-        } else {
-            if (document.exitFullscreen) {
-                document.exitFullscreen();
-            } else if (document.mozCancelFullScreen) {
-                document.mozCancelFullScreen();
-            } else if (document.webkitExitFullscreen) {
-                document.webkitExitFullscreen();
-            } else if (document.msExitFullscreen) {
-                document.msExitFullscreen();
-            }
-        }
-    }
-
-    feed.addEventListener("click", toggleFullScreen);
-    feed.addEventListener("touchstart", toggleFullScreen);
-
-    document.addEventListener("fullscreenchange", function() {
-        isFullscreen = !isFullscreen;
-    });
-});
-
-  // Change feed on space bar press
-    document.addEventListener('keydown', function(event) {
-      if (event.keyCode === 32) { 
-        window.location.href = '/?new=true';
-      }
-    });
-
-  // Show more info
-  document.addEventListener('DOMContentLoaded', function() {
-    const infoText = document.getElementById('info-text');
-    const additionalInfo = document.getElementById('additional-info');
-    const showMore = document.getElementById('show-more');
-    const moreButton = document.getElementById('more-button');
-  
-    // Initial setup
-    additionalInfo.style.height = '0';
-    additionalInfo.style.overflow = 'hidden';
-    additionalInfo.style.transition = 'height 0.3s ease-in-out';
-  
-    showMore.addEventListener('click', function() {
-      if (additionalInfo.style.height === '0px') {
-        const scrollHeight = additionalInfo.scrollHeight;
-        additionalInfo.style.height = `${scrollHeight}px`;
-        showMore.innerHTML = 'less <i style="margin-bottom:20px; color:rgb(53, 53, 53);" id="more-button" class="fa-solid fa-caret-up"></i>';
-      } else {
-        additionalInfo.style.height = '0';
-        showMore.innerHTML = 'more <i style="margin-bottom:20px; color:rgb(53, 53, 53);" id="more-button" class="fa-solid fa-caret-down"></i>';
-      }
-    });
-  });
-
-
-
-      
-    </script>
-</body>
-
-</html>

spaces/CAMP-ViL/Xplainer/description.md

@@ -1,3 +0,0 @@
-This demo provides a playground for testing the model of our paper "Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis", which was accepted for publication at MICCAI 2023. You can test our pre-defined prompts and define your own prompts and diseases.
-
-**Paper**: [arxiv](https://arxiv.org/pdf/2303.13391.pdf), **Code**: [Github](https://github.com/ChantalMP/Xplainer)

spaces/CVPR/LIVE/thrust/testing/unittest/meta.h

@@ -1,260 +0,0 @@
-/*! \file meta.h
- *  \brief Defines template classes
- *         for metaprogramming in the
- *         unit tests.
- */
-
-#pragma once
-
-namespace unittest
-{
-
-// mark the absence of a type
-struct null_type {}; 
-
-// this type encapsulates a list of
-// up to 10 types
-template<typename T0 = null_type,
-         typename T1 = null_type,
-         typename T2 = null_type,
-         typename T3 = null_type,
-         typename T4 = null_type,
-         typename T5 = null_type,
-         typename T6 = null_type,
-         typename T7 = null_type,
-         typename T8 = null_type,
-         typename T9 = null_type,
-         typename T10 = null_type,
-         typename T11 = null_type,
-         typename T12 = null_type,
-         typename T13 = null_type,
-         typename T14 = null_type,
-         typename T15 = null_type,
-         typename T16 = null_type,
-         typename T17 = null_type,
-         typename T18 = null_type,
-         typename T19 = null_type>
-  struct type_list
-{
-  typedef T0 type_0;
-  typedef T1 type_1;
-  typedef T2 type_2;
-  typedef T3 type_3;
-  typedef T4 type_4;
-  typedef T5 type_5;
-  typedef T6 type_6;
-  typedef T7 type_7;
-  typedef T8 type_8;
-  typedef T9 type_9;
-  typedef T10 type_10;
-  typedef T11 type_11;
-  typedef T12 type_12;
-  typedef T13 type_13;
-  typedef T14 type_14;
-  typedef T15 type_15;
-  typedef T16 type_16;
-  typedef T17 type_17;
-  typedef T18 type_18;
-  typedef T19 type_19;
-};
-
-// this type provides a way of indexing
-// into a type_list
-template<typename List, unsigned int i>
-  struct get_type
-{
-  typedef null_type type;
-};
-
-template<typename List>  struct get_type<List,0> { typedef typename List::type_0 type; };
-template<typename List>  struct get_type<List,1> { typedef typename List::type_1 type; };
-template<typename List>  struct get_type<List,2> { typedef typename List::type_2 type; };
-template<typename List>  struct get_type<List,3> { typedef typename List::type_3 type; };
-template<typename List>  struct get_type<List,4> { typedef typename List::type_4 type; };
-template<typename List>  struct get_type<List,5> { typedef typename List::type_5 type; };
-template<typename List>  struct get_type<List,6> { typedef typename List::type_6 type; };
-template<typename List>  struct get_type<List,7> { typedef typename List::type_7 type; };
-template<typename List>  struct get_type<List,8> { typedef typename List::type_8 type; };
-template<typename List>  struct get_type<List,9> { typedef typename List::type_9 type; };
-template<typename List>  struct get_type<List,10> { typedef typename List::type_10 type; };
-template<typename List>  struct get_type<List,11> { typedef typename List::type_11 type; };
-template<typename List>  struct get_type<List,12> { typedef typename List::type_12 type; };
-template<typename List>  struct get_type<List,13> { typedef typename List::type_13 type; };
-template<typename List>  struct get_type<List,14> { typedef typename List::type_14 type; };
-template<typename List>  struct get_type<List,15> { typedef typename List::type_15 type; };
-template<typename List>  struct get_type<List,16> { typedef typename List::type_16 type; };
-template<typename List>  struct get_type<List,17> { typedef typename List::type_17 type; };
-template<typename List>  struct get_type<List,18> { typedef typename List::type_18 type; };
-template<typename List>  struct get_type<List,19> { typedef typename List::type_19 type; };
-
-// this type and its specialization provides a way to
-// iterate over a type_list, and
-// applying a unary function to each type
-template<typename TypeList,
-         template <typename> class Function,
-         typename T,
-         unsigned int i = 0>
-  struct for_each_type
-{
-  template<typename U>
-    void operator()(U n)
-  {
-    // run the function on type T
-    Function<T> f;
-    f(n);
-
-    // get the next type
-    typedef typename get_type<TypeList,i+1>::type next_type;
-
-    // recurse to i + 1
-    for_each_type<TypeList, Function, next_type, i + 1> loop;
-    loop(n);
-  }
-
-  void operator()(void)
-  {
-    // run the function on type T
-    Function<T> f;
-    f();
-
-    // get the next type
-    typedef typename get_type<TypeList,i+1>::type next_type;
-
-    // recurse to i + 1
-    for_each_type<TypeList, Function, next_type, i + 1> loop;
-    loop();
-  }
-};
-
-// terminal case: do nothing when encountering null_type
-template<typename TypeList,
-         template <typename> class Function,
-         unsigned int i>
-  struct for_each_type<TypeList, Function, null_type, i>
-{
-  template<typename U>
-    void operator()(U)
-  {
-    // no-op
-  }
-
-  void operator()(void)
-  {
-    // no-op
-  }
-};
-
-// this type and its specialization instantiates
-// a template by applying T to Template.
-// if T == null_type, then its result is also null_type
-template<template <typename> class Template,
-         typename T>
-  struct ApplyTemplate1
-{
-  typedef Template<T> type;
-};
-
-template<template <typename> class Template>
-  struct ApplyTemplate1<Template, null_type>
-{
-  typedef null_type type;
-};
-
-// this type and its specializations instantiates
-// a template by applying T1 & T2 to Template.
-// if either T1 or T2 == null_type, then its result
-// is also null_type
-template<template <typename,typename> class Template,
-         typename T1,
-         typename T2>
-  struct ApplyTemplate2
-{
-  typedef Template<T1,T2> type;
-};
-
-template<template <typename,typename> class Template,
-         typename T>
-  struct ApplyTemplate2<Template, T, null_type>
-{
-  typedef null_type type;
-};
-
-template<template <typename,typename> class Template,
-         typename T>
-  struct ApplyTemplate2<Template, null_type, T>
-{
-  typedef null_type type;
-};
-
-template<template <typename,typename> class Template>
-  struct ApplyTemplate2<Template, null_type, null_type>
-{
-  typedef null_type type;
-};
-
-// this type creates a new type_list by applying a Template to each of
-// the Type_list's types
-template<typename TypeList,
-         template <typename> class Template>
-  struct transform1
-{
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,0>::type>::type type_0;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,1>::type>::type type_1;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,2>::type>::type type_2;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,3>::type>::type type_3;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,4>::type>::type type_4;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,5>::type>::type type_5;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,6>::type>::type type_6;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,7>::type>::type type_7;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,8>::type>::type type_8;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,9>::type>::type type_9;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,10>::type>::type type_10;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,11>::type>::type type_11;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,12>::type>::type type_12;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,13>::type>::type type_13;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,14>::type>::type type_14;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,15>::type>::type type_15;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,16>::type>::type type_16;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,17>::type>::type type_17;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,18>::type>::type type_18;
-  typedef typename ApplyTemplate1<Template, typename get_type<TypeList,19>::type>::type type_19;
-
-  typedef type_list<type_0, type_1, type_2, type_3, type_4, type_5, type_6, type_7, type_8, type_9,
-                    type_10, type_11, type_12, type_13, type_14, type_15, type_16, type_17, type_18, type_19> type;
-};
-
-// this type creates a new type_list by applying a Template to each of
-// two type_list's types
-template<typename TypeList1,
-         typename TypeList2,
-         template <typename,typename> class Template>
-  struct transform2
-{
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,0>::type, typename get_type<TypeList2,0>::type>::type type_0;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,1>::type, typename get_type<TypeList2,1>::type>::type type_1;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,2>::type, typename get_type<TypeList2,2>::type>::type type_2;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,3>::type, typename get_type<TypeList2,3>::type>::type type_3;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,4>::type, typename get_type<TypeList2,4>::type>::type type_4;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,5>::type, typename get_type<TypeList2,5>::type>::type type_5;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,6>::type, typename get_type<TypeList2,6>::type>::type type_6;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,7>::type, typename get_type<TypeList2,7>::type>::type type_7;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,8>::type, typename get_type<TypeList2,8>::type>::type type_8;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,9>::type, typename get_type<TypeList2,9>::type>::type type_9;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,10>::type, typename get_type<TypeList2,10>::type>::type type_10;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,11>::type, typename get_type<TypeList2,11>::type>::type type_11;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,12>::type, typename get_type<TypeList2,12>::type>::type type_12;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,13>::type, typename get_type<TypeList2,13>::type>::type type_13;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,14>::type, typename get_type<TypeList2,14>::type>::type type_14;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,15>::type, typename get_type<TypeList2,15>::type>::type type_15;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,16>::type, typename get_type<TypeList2,16>::type>::type type_16;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,17>::type, typename get_type<TypeList2,17>::type>::type type_17;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,18>::type, typename get_type<TypeList2,18>::type>::type type_18;
-  typedef typename ApplyTemplate2<Template, typename get_type<TypeList1,19>::type, typename get_type<TypeList2,19>::type>::type type_19;
-  
-
-  typedef type_list<type_0, type_1, type_2, type_3, type_4, type_5, type_6, type_7, type_8, type_9,
-                    type_10, type_11, type_12, type_13, type_14, type_15, type_16, type_17, type_18, type_19> type;
-};
-
-} // end unittest
-

spaces/CVPR/LIVE/thrust/thrust/detail/pointer.h

@@ -1,253 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/iterator/iterator_adaptor.h>
-#include <thrust/iterator/detail/iterator_traversal_tags.h>
-#include <thrust/detail/type_traits/pointer_traits.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/detail/reference_forward_declaration.h>
-#include <ostream>
-
-
-namespace thrust
-{
-
-// declare pointer with default values of template parameters
-template<typename Element, typename Tag, typename Reference = use_default, typename Derived = use_default> class pointer;
-
-} // end thrust
-
-
-// specialize thrust::iterator_traits to avoid problems with the name of
-// pointer's constructor shadowing its nested pointer type
-// do this before pointer is defined so the specialization is correctly
-// used inside the definition
-namespace thrust
-{
-
-template<typename Element, typename Tag, typename Reference, typename Derived>
-  struct iterator_traits<thrust::pointer<Element,Tag,Reference,Derived> >
-{
-  private:
-    typedef thrust::pointer<Element,Tag,Reference,Derived> ptr;
-
-  public:
-    typedef typename ptr::iterator_category iterator_category;
-    typedef typename ptr::value_type        value_type;
-    typedef typename ptr::difference_type   difference_type;
-    // XXX implement this type (the result of operator->) later
-    typedef void                             pointer;
-    typedef typename ptr::reference         reference;
-}; // end iterator_traits
-
-} // end thrust
-
-
-namespace thrust
-{
-
-namespace detail
-{
-
-// this metafunction computes the type of iterator_adaptor thrust::pointer should inherit from
-template<typename Element, typename Tag, typename Reference, typename Derived>
-  struct pointer_base
-{
-  // void pointers should have no element type
-  // note that we remove_cv from the Element type to get the value_type
-  typedef typename thrust::detail::eval_if<
-    thrust::detail::is_void<typename thrust::detail::remove_const<Element>::type>::value,
-    thrust::detail::identity_<void>,
-    thrust::detail::remove_cv<Element>
-  >::type value_type;
-
-  // if no Derived type is given, just use pointer
-  typedef typename thrust::detail::eval_if<
-    thrust::detail::is_same<Derived,use_default>::value,
-    thrust::detail::identity_<pointer<Element,Tag,Reference,Derived> >,
-    thrust::detail::identity_<Derived>
-  >::type derived_type;
-
-  // void pointers should have no reference type
-  // if no Reference type is given, just use reference
-  typedef typename thrust::detail::eval_if<
-    thrust::detail::is_void<typename thrust::detail::remove_const<Element>::type>::value,
-    thrust::detail::identity_<void>,
-    thrust::detail::eval_if<
-      thrust::detail::is_same<Reference,use_default>::value,
-      thrust::detail::identity_<reference<Element,derived_type> >,
-      thrust::detail::identity_<Reference>
-    >
-  >::type reference_arg;
-
-  typedef thrust::iterator_adaptor<
-    derived_type,                        // pass along the type of our Derived class to iterator_adaptor
-    Element *,                           // we adapt a raw pointer
-    value_type,                          // the value type
-    Tag,                                 // system tag
-    thrust::random_access_traversal_tag, // pointers have random access traversal
-    reference_arg,                       // pass along our Reference type
-    std::ptrdiff_t
-  > type;
-}; // end pointer_base
-
-
-} // end detail
-
-
-// the base type for all of thrust's tagged pointers.
-// for reasonable pointer-like semantics, derived types should reimplement the following:
-// 1. no-argument constructor
-// 2. constructor from OtherElement *
-// 3. constructor from OtherPointer related by convertibility
-// 4. constructor from OtherPointer to void
-// 5. assignment from OtherPointer related by convertibility
-// These should just call the corresponding members of pointer.
-template<typename Element, typename Tag, typename Reference, typename Derived>
-  class pointer
-    : public thrust::detail::pointer_base<Element,Tag,Reference,Derived>::type
-{
-  private:
-    typedef typename thrust::detail::pointer_base<Element,Tag,Reference,Derived>::type         super_t;
-
-    typedef typename thrust::detail::pointer_base<Element,Tag,Reference,Derived>::derived_type derived_type;
-
-    // friend iterator_core_access to give it access to dereference
-    friend class thrust::iterator_core_access;
-
-    __host__ __device__
-    typename super_t::reference dereference() const;
-
-    // don't provide access to this part of super_t's interface
-    using super_t::base;
-    using typename super_t::base_type;
-
-  public:
-    typedef typename super_t::base_type raw_pointer;
-
-    // constructors
-
-    __host__ __device__
-    pointer();
-
-    #if THRUST_CPP_DIALECT >= 2011
-    // NOTE: This is needed so that Thrust smart pointers can be used in
-    // `std::unique_ptr`.
-    __host__ __device__
-    pointer(decltype(nullptr));
-    #endif
-
-    // OtherValue shall be convertible to Value
-    // XXX consider making the pointer implementation a template parameter which defaults to Element *
-    template<typename OtherElement>
-    __host__ __device__
-    explicit pointer(OtherElement *ptr);
-
-    // OtherPointer's element_type shall be convertible to Element
-    // OtherPointer's system shall be convertible to Tag
-    template<typename OtherPointer>
-    __host__ __device__
-    pointer(const OtherPointer &other,
-            typename thrust::detail::enable_if_pointer_is_convertible<
-              OtherPointer,
-              pointer<Element,Tag,Reference,Derived>
-            >::type * = 0);
-
-    // OtherPointer's element_type shall be void
-    // OtherPointer's system shall be convertible to Tag
-    template<typename OtherPointer>
-    __host__ __device__
-    explicit
-    pointer(const OtherPointer &other,
-            typename thrust::detail::enable_if_void_pointer_is_system_convertible<
-              OtherPointer,
-              pointer<Element,Tag,Reference,Derived>
-            >::type * = 0);
-
-    // assignment
-
-    #if THRUST_CPP_DIALECT >= 2011
-    // NOTE: This is needed so that Thrust smart pointers can be used in
-    // `std::unique_ptr`.
-    __host__ __device__
-    derived_type& operator=(decltype(nullptr));
-    #endif
-
-    // OtherPointer's element_type shall be convertible to Element
-    // OtherPointer's system shall be convertible to Tag
-    template<typename OtherPointer>
-    __host__ __device__
-    typename thrust::detail::enable_if_pointer_is_convertible<
-      OtherPointer,
-      pointer,
-      derived_type &
-    >::type
-    operator=(const OtherPointer &other);
-
-    // observers
-
-    __host__ __device__
-    Element *get() const;
-
-    #if THRUST_CPP_DIALECT >= 2011
-    // NOTE: This is needed so that Thrust smart pointers can be used in
-    // `std::unique_ptr`.
-    __host__ __device__
-    explicit operator bool() const;
-    #endif
-
-    __host__ __device__
-    static derived_type pointer_to(typename thrust::detail::pointer_traits_detail::pointer_to_param<Element>::type r)
-    {
-      return thrust::detail::pointer_traits<derived_type>::pointer_to(r);
-    }
-}; // end pointer
-
-// Output stream operator
-template<typename Element, typename Tag, typename Reference, typename Derived,
-         typename charT, typename traits>
-__host__
-std::basic_ostream<charT, traits> &
-operator<<(std::basic_ostream<charT, traits> &os,
-           const pointer<Element, Tag, Reference, Derived> &p);
-
-#if THRUST_CPP_DIALECT >= 2011
-// NOTE: This is needed so that Thrust smart pointers can be used in
-// `std::unique_ptr`.
-template <typename Element, typename Tag, typename Reference, typename Derived>
-__host__ __device__
-bool operator==(decltype(nullptr), pointer<Element, Tag, Reference, Derived> p);
-
-template <typename Element, typename Tag, typename Reference, typename Derived>
-__host__ __device__
-bool operator==(pointer<Element, Tag, Reference, Derived> p, decltype(nullptr));
-
-template <typename Element, typename Tag, typename Reference, typename Derived>
-__host__ __device__
-bool operator!=(decltype(nullptr), pointer<Element, Tag, Reference, Derived> p);
-
-template <typename Element, typename Tag, typename Reference, typename Derived>
-__host__ __device__
-bool operator!=(pointer<Element, Tag, Reference, Derived> p, decltype(nullptr));
-#endif
-
-} // end thrust
-
-#include <thrust/detail/pointer.inl>
-

spaces/CVPR/drawings-to-human/README.md

@@ -1,12 +0,0 @@
----
-title: Drawings to Human
-emoji: ✍️🧍🏽‍♀️🧍🏻
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.24
-pinned: false
-app_file: main.py
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/lama-example/saicinpainting/training/visualizers/noop.py

@@ -1,9 +0,0 @@
-from saicinpainting.training.visualizers.base import BaseVisualizer
-
-
-class NoopVisualizer(BaseVisualizer):
-    def __init__(self, *args, **kwargs):
-        pass
-
-    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
-        pass

spaces/CarperAI/StableVicuna/README.md

@@ -1,13 +0,0 @@
----
-title: StableVicuna
-emoji: 🦙
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Chris4K/llms_compare/Wondershare-Dvd-Slideshow-Builder-Deluxe-3d-Style-Pack-Registration-Code-Keygen.md

@@ -1,74 +0,0 @@
-## Wondershare Dvd Slideshow Builder Deluxe 3d Style Pack Registration Code Keygen
-
-
-
- 
-
- ![Wondershare Dvd Slideshow Builder Deluxe 3d Style Pack Registration Code Keygen](https://3.bp.blogspot.com/-D0F19vRb9Xw/Uwv0JAKVuCI/AAAAAAAACOE/WsptycKZY_0/s1600/3D+Style+Pack+Box.jpg)
-
- 
-
- 
-
-**CLICK HERE ····· [https://urluso.com/2tBNxk](https://urluso.com/2tBNxk)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# How to Make Amazing 3D Slideshows with Wondershare DVD Slideshow Builder Deluxe and 3D Style Pack
-
-  
-
-If you want to create stunning 3D slideshows from your photos and videos, you need a powerful and easy-to-use software that can handle the task. Wondershare DVD Slideshow Builder Deluxe is a professional slideshow maker that lets you turn your photos and videos into amazing DVD slideshows with music, transitions, effects, and more. But what if you want to add some extra flair to your slideshows with 3D movie effects? That's where Wondershare 3D Style Pack comes in.
-
-  
-
-Wondershare 3D Style Pack is an exclusive expansion pack for Wondershare DVD Slideshow Builder Deluxe that gives you access to more than 10 "Styles" of popular 3D video effects, such as 3D Cube, Photo Flow, 3D Wall, 3D Square, 3D Carousel, 3D Box, and so on. With these 3D styles, you can make your slideshows more dynamic and eye-catching. You can also customize the 3D effects with parameters like angle, distance, depth, and color.
-
-  
-
-To use Wondershare 3D Style Pack, you need to purchase both the software and the style pack from the official website[^1^]. Then, you can download and install them on your computer. After that, you can launch Wondershare DVD Slideshow Builder Deluxe and choose Standard mode. Then, you can import your photos and videos to the storyboard and apply the 3D styles to them. You can preview the effects in real time and adjust them as you like. Finally, you can burn your slideshow to DVD or save it as a video file.
-
-  
-
-Wondershare 3D Style Pack is a great way to enhance your slideshows with 3D movie effects. It's compatible with Windows XP/Vista/7/8/10 and supports various formats of photos and videos. You can also get free updates and technical support from Wondershare. If you want to try it before buying it, you can download a free trial version of Wondershare DVD Slideshow Builder Deluxe from the official website[^1^] and use some of the 3D styles for free.
-
-  
-
-So what are you waiting for? Get Wondershare DVD Slideshow Builder Deluxe and 3D Style Pack today and start making amazing 3D slideshows with your photos and videos!
-
-  
-
-## How to Use Wondershare DVD Slideshow Builder Deluxe
-
-  
-
-Wondershare DVD Slideshow Builder Deluxe is a user-friendly and versatile software that lets you create professional-looking slideshows with your photos and videos. You can use it to make slideshows for various occasions, such as weddings, birthdays, anniversaries, vacations, and more. You can also use it to make slideshows for business presentations, education, and marketing.
-
-  
-
-To use Wondershare DVD Slideshow Builder Deluxe, you need to download and install it on your computer. Then, you can launch it and choose between two modes: Advanced Mode and Standard Mode[^1^]. Advanced Mode gives you more control and customization options for your slideshows, while Standard Mode is simpler and faster to use. You can switch between the modes at any time.
-
-  
-
-In both modes, you can import your photos and videos to the storyboard by clicking the Add Files button or dragging and dropping them. You can also add background music, voiceovers, text, clipart, and other elements to your slideshows. You can edit your photos and videos with basic tools like crop, rotate, trim, adjust brightness, contrast, saturation, etc. You can also apply transitions, effects, filters, and themes to your slideshows to make them more attractive.
-
-  
-
-Once you are satisfied with your slideshows, you can preview them in full screen and make any changes if needed. Then, you can save your slideshows as a video file in various formats like MP4, AVI, WMV, MOV, FLV, etc. You can also burn your slideshows to DVD discs or ISO files with a built-in DVD menu. You can also share your slideshows online via YouTube, Facebook, Vimeo, etc.
-
- 145887f19f
-
- 
-
- 
-

spaces/Cong723/gpt-academic-public/crazy_functions/crazy_functions_test.py

@@ -1,130 +0,0 @@
-"""
-这是什么？
-    这个文件用于函数插件的单元测试
-    运行方法 python crazy_functions/crazy_functions_test.py
-"""
-
-def validate_path():
-    import os, sys
-    dir_name = os.path.dirname(__file__)
-    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-    os.chdir(root_dir_assume)
-    sys.path.append(root_dir_assume)
-    
-validate_path() # validate path so you can run from base directory
-from colorful import *
-from toolbox import get_conf, ChatBotWithCookies
-proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
-    get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
-
-llm_kwargs = {
-    'api_key': API_KEY,
-    'llm_model': LLM_MODEL,
-    'top_p':1.0, 
-    'max_length': None,
-    'temperature':1.0,
-}
-plugin_kwargs = { }
-chatbot = ChatBotWithCookies(llm_kwargs)
-history = []
-system_prompt = "Serve me as a writing and programming assistant."
-web_port = 1024
-
-
-def test_解析一个Python项目():
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    txt = "crazy_functions/test_project/python/dqn"
-    for cookies, cb, hist, msg in 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_解析一个Cpp项目():
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    txt = "crazy_functions/test_project/cpp/cppipc"
-    for cookies, cb, hist, msg in 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_Latex英文润色():
-    from crazy_functions.Latex全文润色 import Latex英文润色
-    txt = "crazy_functions/test_project/latex/attention"
-    for cookies, cb, hist, msg in Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_Markdown中译英():
-    from crazy_functions.批量Markdown翻译 import Markdown中译英
-    txt = "README.md"
-    for cookies, cb, hist, msg in Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_批量翻译PDF文档():
-    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
-    txt = "crazy_functions/test_project/pdf_and_word"
-    for cookies, cb, hist, msg in 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_谷歌检索小助手():
-    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
-    txt = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG="
-    for cookies, cb, hist, msg in 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_总结word文档():
-    from crazy_functions.总结word文档 import 总结word文档
-    txt = "crazy_functions/test_project/pdf_and_word"
-    for cookies, cb, hist, msg in 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_下载arxiv论文并翻译摘要():
-    from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
-    txt = "1812.10695"
-    for cookies, cb, hist, msg in 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_联网回答问题():
-    from crazy_functions.联网的ChatGPT import 连接网络回答问题
-    # txt = "“我们称之为高效”是什么梗？"
-    # >>        从第0份、第1份、第2份搜索结果可以看出，“我们称之为高效”是指在游戏社区中，用户们用来形容一些游戏策略或行为非常高效且能够带来好的效果的用语。这个用语最初可能是在群星（Stellaris）这个游戏里面流行起来的，后来也传播到了其他游戏中，比如巨像（Titan）等游戏。其中第1份搜索结果中的一篇文章也指出，“我们称之为高效”这 一用语来源于群星（Stellaris）游戏中的一个情节。
-    # txt = "为什么说枪毙P社玩家没有一个冤枉的？"
-    # >>        它们都是关于一个知乎用户所发的帖子，引用了一群游戏玩家对于需要对P社玩家进行枪毙的讨论，这个话题的本质是玩家们对于P 社游戏中的政治与历史元素的不同看法，以及其中不少玩家以极端立场宣扬的想法和言论，因此有人就以枪毙这些玩家来回应此类言论。但是这个话题本身并没有实质内容，只是一个玩笑或者恶搞，并不应该被当做真实的态度或者观点，因此这种说法没有实际意义。
-    # txt = "谁是应急食品？"
-    # >>        '根据以上搜索结果可以得知，应急食品是“原神”游戏中的角色派蒙的外号。'
-    # txt = "道路千万条，安全第一条。后面两句是？"
-    # >>        '行车不规范，亲人两行泪。'
-    # txt = "What is in the canister?"
-    # >>        Rainbow Six Siege 游戏中 Smoke 的 Canister 中装有何种物质相关的官方信息。
-    # txt = "失败的man是什么?"
-    # >>        根据第1份搜索结果，可以得知失败的man是指一位在B站购买了蜘蛛侠COS服后穿上后被网友嘲笑的UP主，而“失败的man”是蜘蛛侠英文名“spiderman”的谐音梗，并且网友们还 给这位UP主起了“苍蝇侠”的外号。因此，失败的man是指这位UP主在穿上蜘蛛侠COS服后被网友嘲笑的情况。
-    # txt = "老六是什么，起源于哪里？"
-    # >>        老六是网络流行语，最初起源于游戏《CSGO》，指游戏中玩家中独来独往、游离于队伍之外的“自由人”或玩得比较菜或者玩得比较阴险的人 ，后来逐渐演变成指玩得比较阴险的玩家。
-    # txt = "罗小黑战记因为什么经常被吐槽？"
-    # >>        3. 更新速度。罗小黑战记的更新时间不定，时而快时而慢，给观众留下了等待的时间过长的印象。
-    # txt = "沙特、伊朗最近的关系如何？"
-    # >>        最近在中国的斡旋下，沙特和伊朗于3月10日达成了恢复两国外交关系的协议，这表明两国关系已经重新回到正常化状态。
-    # txt = "You should have gone for the head. What does that mean?"
-    # >>        The phrase "You should have gone for the head" is a quote from the Marvel movies, Avengers: Infinity War and Avengers: Endgame. It was spoken by the character Thanos in Infinity War and by Thor in Endgame.
-    txt = "AutoGPT是什么？"
-    # >>        AutoGPT是一个基于GPT-4语言模型的开源应用程序。它可以根据用户需求自主执行任务，包括事件分析、营销方案撰写、代码编程、数学运算等等，并完全不需要用户插手。它可以自己思考，给出实现的步骤和实现细节，甚至可以自问自答执 行任务。最近它在GitHub上爆火，成为了业内最热门的项目之一。
-    # txt = "钟离带什么圣遗物？"
-    for cookies, cb, hist, msg in 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): 
-        print("当前问答：", cb[-1][-1].replace("\n"," "))
-    for i, it in enumerate(cb): print亮蓝(it[0]); print亮黄(it[1])
-
-def test_解析ipynb文件():
-    from crazy_functions.解析JupyterNotebook import 解析ipynb文件
-    txt = "crazy_functions/test_samples"
-    for cookies, cb, hist, msg in 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-
-# test_解析一个Python项目()
-# test_Latex英文润色()
-# test_Markdown中译英()
-# test_批量翻译PDF文档()
-# test_谷歌检索小助手()
-# test_总结word文档()
-# test_下载arxiv论文并翻译摘要()
-# test_解析一个Cpp项目()
-# test_联网回答问题()
-test_解析ipynb文件()
-
-input("程序完成，回车退出。")
-print("退出。")

spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/ip_segment.py

@@ -1,123 +0,0 @@
-import cv2
-import numpy as np
-import shutil
-import os
-from os.path import join as pjoin
-
-
-def segment_img(org, segment_size, output_path, overlap=100):
-    if not os.path.exists(output_path):
-        os.mkdir(output_path)
-
-    height, width = np.shape(org)[0], np.shape(org)[1]
-    top = 0
-    bottom = segment_size
-    segment_no = 0
-    while top < height and bottom < height:
-        segment = org[top:bottom]
-        cv2.imwrite(os.path.join(output_path, str(segment_no) + '.png'), segment)
-        segment_no += 1
-        top += segment_size - overlap
-        bottom = bottom + segment_size - overlap if bottom + segment_size - overlap <= height else height
-
-
-def clipping(img, components, pad=0, show=False):
-    """
-    :param adjust: shrink(negative) or expand(positive) the bounding box
-    :param img: original image
-    :param corners: ((column_min, row_min),(column_max, row_max))
-    :return: list of clipping images
-    """
-    clips = []
-    for component in components:
-        clip = component.compo_clipping(img, pad=pad)
-        clips.append(clip)
-        if show:
-            cv2.imshow('clipping', clip)
-            cv2.waitKey()
-    return clips
-
-
-def dissemble_clip_img_hollow(clip_root, org, compos):
-    if os.path.exists(clip_root):
-        shutil.rmtree(clip_root)
-    os.mkdir(clip_root)
-    cls_dirs = []
-
-    bkg = org.copy()
-    hollow_out = np.ones(bkg.shape[:2], dtype=np.uint8) * 255
-    for compo in compos:
-        cls = compo.category
-        c_root = pjoin(clip_root, cls)
-        c_path = pjoin(c_root, str(compo.id) + '.jpg')
-        if cls not in cls_dirs:
-            os.mkdir(c_root)
-            cls_dirs.append(cls)
-        clip = compo.compo_clipping(org)
-        cv2.imwrite(c_path, clip)
-
-        col_min, row_min, col_max, row_max = compo.put_bbox()
-        hollow_out[row_min: row_max, col_min: col_max] = 0
-
-    bkg = cv2.merge((bkg, hollow_out))
-    cv2.imwrite(os.path.join(clip_root, 'bkg.png'), bkg)
-
-
-def dissemble_clip_img_fill(clip_root, org, compos, flag='most'):
-
-    def average_pix_around(pad=6, offset=3):
-        up = row_min - pad if row_min - pad >= 0 else 0
-        left = col_min - pad if col_min - pad >= 0 else 0
-        bottom = row_max + pad if row_max + pad < org.shape[0] - 1 else org.shape[0] - 1
-        right = col_max + pad if col_max + pad < org.shape[1] - 1 else org.shape[1] - 1
-
-        average = []
-        for i in range(3):
-            avg_up = np.average(org[up:row_min - offset, left:right, i])
-            avg_bot = np.average(org[row_max + offset:bottom, left:right, i])
-            avg_left = np.average(org[up:bottom, left:col_min - offset, i])
-            avg_right = np.average(org[up:bottom, col_max + offset:right, i])
-            average.append(int((avg_up + avg_bot + avg_left + avg_right)/4))
-        return average
-
-    def most_pix_around(pad=6, offset=2):
-        up = row_min - pad if row_min - pad >= 0 else 0
-        left = col_min - pad if col_min - pad >= 0 else 0
-        bottom = row_max + pad if row_max + pad < org.shape[0] - 1 else org.shape[0] - 1
-        right = col_max + pad if col_max + pad < org.shape[1] - 1 else org.shape[1] - 1
-
-        most = []
-        for i in range(3):
-            val = np.concatenate((org[up:row_min - offset, left:right, i].flatten(),
-                            org[row_max + offset:bottom, left:right, i].flatten(),
-                            org[up:bottom, left:col_min - offset, i].flatten(),
-                            org[up:bottom, col_max + offset:right, i].flatten()))
-            # print(val)
-            # print(np.argmax(np.bincount(val)))
-            most.append(int(np.argmax(np.bincount(val))))
-        return most
-
-    if os.path.exists(clip_root):
-        shutil.rmtree(clip_root)
-    os.mkdir(clip_root)
-    cls_dirs = []
-
-    bkg = org.copy()
-    for compo in compos:
-        cls = compo.category
-        c_root = pjoin(clip_root, cls)
-        c_path = pjoin(c_root, str(compo.id) + '.jpg')
-        if cls not in cls_dirs:
-            os.mkdir(c_root)
-            cls_dirs.append(cls)
-        clip = compo.compo_clipping(org)
-        cv2.imwrite(c_path, clip)
-
-        col_min, row_min, col_max, row_max = compo.put_bbox()
-        if flag == 'average':
-            color = average_pix_around()
-        elif flag == 'most':
-            color = most_pix_around()
-        cv2.rectangle(bkg, (col_min, row_min), (col_max, row_max), color, -1)
-
-    cv2.imwrite(os.path.join(clip_root, 'bkg.png'), bkg)